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Estimación de la susceptibilidad a movimientos en masa superficiales por medio de un análisis de regresión espacial local. Aplicación para un tramo de la cuenca media del río Chicamocha

dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.contributor.advisorMoreno Murillo, Juan Manuel
dc.contributor.authorVelásquez Giraldo, Diego Felipe
dc.date.accessioned2024-04-18T20:00:34Z
dc.date.available2024-04-18T20:00:34Z
dc.date.issued2024-04
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/85950
dc.descriptionilustraciones, diagramas, fotografías
dc.description.abstractEn este trabajo de investigación se presenta la estimación de la susceptibilidad a movimientos en masa superficiales mediante un análisis de regresión espacial local, específicamente un modelo de regresión logística geográficamente ponderada (GWLR) que tiene en cuenta las relaciones no estacionarias de los factores que influyen en la ocurrencia de movimientos en masa en una zona. Se aplicó este método en un tramo de la cuenca media del río Chicamocha, ubicada en el departamento de Boyacá, que fue seleccionada debido a sus características geológicas y geomorfológicas, así como a la evidente inestabilidad observada en la región. Además, se calibró un modelo de regresión global (regresión logística convencional, LR) para determinar las ventajas y desventajas de cada método en estudios de susceptibilidad. Se consideraron variables independientes como la litología, proximidad a fallas, pendiente, curvatura, rugosidad del terreno, TWI, SPI, proximidad y densidad de drenaje, precipitación, proximidad a vías, cobertura de la tierra, NDVI y zonas con predominio de procesos erosivos. Las estimaciones revelan que el 28% del área de estudio presenta una alta y muy alta susceptibilidad a deslizamientos superficiales y un 25% a movimientos tipo flujo. Se encontraron diferencias significativas en el rendimiento entre los modelos de regresión locales y globales, de acuerdo con la mejora de los estadísticos de grado de ajuste (devianza, AIC y McFadden pseudo R2) y los valores de tasa de predicción (ROC-AUC). El análisis de regresión espacial local también revela que la contribución de las variables independientes en la ocurrencia de zonas inestables varia a lo largo de la zona de estudio. Los resultados permiten concluir que el modelo GWLR ofrece una mejora potencial en la estimación de la susceptibilidad a movimientos en masa en el contexto colombiano en comparación con los métodos convencionales de regresión global (LR). (Texto tomado de la fuente).
dc.description.abstractThis research presents the estimation of the susceptibility to shallow mass movements by means of a local spatial regression analysis, specifically a geographically weighted logistic regression model (GWLR) that considers the nonstationary relationships of the factors that influence the occurrence of mass movements. This method was applied in a section of the middle basin of the Chicamocha river, located in the department of Boyacá, which was selected due to its geological and geomorphological characteristics, as well as the evident instability observed in the region. In addition, a global regression model (conventional logistic regression, LR) was calibrated to determine the advantages and disadvantages of each method in susceptibility studies. Independent variables such as lithology, proximity to faults, slope, curvature, terrain roughness, TWI, SPI, drainage proximity and density, rainfall, proximity to roads, land use, NDVI and areas with mainly erosive processes were considered. Estimates reveal that 28% of the study area has high and very high susceptibility to shallow landslides and 25% to flows and avalanches (flow-type movements). Significant differences in performance were found between local and global regression models, according to improved goodness-of-fit criteria (deviance, AIC, and McFadden pseudo R2) and prediction rate values (ROC-AUC). The local spatial regression analysis also reveals that the contribution of the independent variables in the occurrence of unstable zones varies across the study area. The results allow us to conclude that the GWLR model offers a potential improvement in the estimation of susceptibility to mass movements in the colombian context compared to conventional global regression (LR) methods.
dc.format.extentxxii, 216 páginas
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.publisherUniversidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología
dc.titleEstimación de la susceptibilidad a movimientos en masa superficiales por medio de un análisis de regresión espacial local. Aplicación para un tramo de la cuenca media del río Chicamocha
dc.typeTrabajo de grado - Maestría
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.publisher.programBogotá - Ciencias - Maestría en Ciencias - Geología
dc.coverage.countryColombia
dc.coverage.tgnhttp://vocab.getty.edu/page/tgn/1000050
dc.description.degreelevelMaestría
dc.description.degreenameMagíster en Ciencias - Geología
dc.description.researchareaGeología ambiental y geoamenazas
dc.identifier.instnameUniversidad Nacional de Colombia
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourlhttps://repositorio.unal.edu.co/
dc.publisher.facultyFacultad de Ciencias
dc.publisher.placeBogotá, Colombia
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotá
dc.relation.referencesAbbaszadeh Shahri, A., Spross, J., Johansson, F., & Larsson, S. (2019). Landslide susceptibility hazard map in southwest Sweden using artificial neural network. Catena, 183(June), 104225. https://doi.org/10.1016/j.catena.2019.104225
dc.relation.referencesAkaike, H. (1973). Information theory and an extension of the maximum likelihood principle. Proceedings of the 2nd international symposium on information theory. Second International Symposium on Information Theory, 267–281.
dc.relation.referencesAkgun, A. (2012). A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: A case study at İzmir, Turkey. Landslides, 9(1), 93–106. https://doi.org/10.1007/s10346-011-0283-7
dc.relation.referencesAkgün, A., & Bulut, F. (2007). GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Environmental Geology, 51(8), 1377–1387. https://doi.org/10.1007/s00254-006-0435-6
dc.relation.referencesAlbuquerque, P., Medina, F., & Da Silva, A. (2017). Geographically Weighted Logistic Regresiion Applied to Credit Scoring Models. Revista Contabilidade e Financas, 28(73), 93–112. https://doi.org/10.1590/1808-057x201703760
dc.relation.referencesAleotti, P., & Chowdhury, R. (1999). Landslide hazard assessment: Summary review and new perspectives. Bulletin of Engineering Geology and the Environment, 58(1), 21–44. https://doi.org/10.1007/s100640050066
dc.relation.referencesAlkhasawneh, M. S., Ngah, U. K., Tay, L. T., & Isa, N. A. M. (2014). Determination of importance for comprehensive topographic factors on landslide hazard mapping using artificial neural network. Environmental Earth Sciences, 72(3), 787–799. https://doi.org/10.1007/s12665-013-3003-x
dc.relation.referencesAlvarado, B., & Sarmiento, R. (1944). Informe geológico general sobre los yacimientos de hierro, carbón y caliza de la región de Paz de Río (Departamento de Boyacá) (p. 157). Servicio Geológico Nacional (SGNC).
dc.relation.referencesAnis, Z., Wissem, G., Vali, V., Smida, H., & Mohamed Essghaier, G. (2019). GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia. Open Geosciences, 11(1), 708–726. https://doi.org/10.1515/geo-2019-0056
dc.relation.referencesArabameri, A., Pradhan, B., & Rezaei, K. (2019). Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS. Journal of Environmental Management, 232(November 2018), 928–942. https://doi.org/10.1016/j.jenvman.2018.11.110
dc.relation.referencesArabameri, A., Saha, S., Roy, J., Chen, W., Blaschke, T., & Bui, D. T. (2020). Landslide susceptibility evaluation and management using different machine learning methods in the Gallicash River Watershed, Iran. Remote Sensing, 12(3). https://doi.org/10.3390/rs12030475
dc.relation.referencesASF DAAC. (2011). ALOS PALSAR_Radiometric_Terrain_Corrected_high_res. JAXA/METI. https://doi.org/10.5067/Z97HFCNKR6VA
dc.relation.referencesAtkinson, P. M., & Massari, R. (1998). Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Computers and Geosciences, 24(4), 373–385. https://doi.org/10.1016/S0098-3004(97)00117-9
dc.relation.referencesAtkinson, Peter M., German, S. E., Sear, D. A., & Clark, M. J. (2003). Exploring the relations between riverbank erosion and geomorphological controls using geographically weighted logistic regression. Geographical Analysis, 35(1), 58–82. https://doi.org/10.1111/j.1538-4632.2003.tb01101.x
dc.relation.referencesAustin, P. C., & Tu, J. V. (2004). Bootstrap Methods for Developing Predictive Models. American Statistician, 58(2), 131–137. https://doi.org/10.1198/0003130043277
dc.relation.referencesAyalew, L., & Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65(1–2), 15–31. https://doi.org/10.1016/j.geomorph.2004.06.010
dc.relation.referencesBacchini, M., & Zannoni, A. (2003). Relations between rainfall and triggering of debris-flow: Case study of Cancia (Dolomites, Northeastern Italy). Natural Hazards and Earth System Science, 3(1–2), 71–79. https://doi.org/10.5194/nhess-3-71-2003
dc.relation.referencesBaeza, C., & Corominas, J. (2001). Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surface Processes and Landforms, 26(12), 1251–1263. https://doi.org/10.1002/esp.263
dc.relation.referencesBai, S. B., Wang, J., Lü, G. N., Zhou, P. G., Hou, S. S., & Xu, S. N. (2010). GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology, 115(1–2), 23–31. https://doi.org/10.1016/j.geomorph.2009.09.025
dc.relation.referencesBai, S., Wang, J., Thiebes, B., Cheng, C., & Yang, Y. (2014). Analysis of the relationship of landslide occurrence with rainfall: A case study of Wudu County, China. Arabian Journal of Geosciences, 7(4), 1277–1285. https://doi.org/10.1007/s12517-013-0939-9
dc.relation.referencesBegueria, S., & Lorente, A. (1999). Landslide Hazard Mapping By Multivariate Statistics : Comparison of Methods and Case Study in the Spanish Pyrenees. Damocles, 20.
dc.relation.referencesBeven, K. J., & Kirkby, M. J. (1979). A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin, 24(1), 43–69. https://doi.org/10.1080/02626667909491834
dc.relation.referencesBivand, R. S., & Wong, D. W. S. (2018). Comparing implementations of global and local indicators of spatial association. Test, 27(3), 716–748. https://doi.org/10.1007/s11749-018-0599-x
dc.relation.referencesBoardman, J., Parsons, A. J., Holland, R., Holmes, P. J., & Washington, R. (2003). Development of badlands and gullies in the Sneeuberg, Great Karoo, South Africa. Catena, 50(2–4), 165–184. https://doi.org/10.1016/S0341-8162(02)00144-3
dc.relation.referencesBordoni, M., Giuseppina Persichillo, M., Meisina, C., Crema, S., Cavalli, M., Bartelletti, C., Galanti, Y., Barsanti, M., Giannecchini, R., & D’Amato Avanzi, G. (2018). Estimation of the susceptibility of a road network to shallow landslides with the integration of the sediment connectivity. Natural Hazards and Earth System Sciences, 18(6), 1735–1758. https://doi.org/10.5194/nhess-18-1735-2018
dc.relation.referencesBorgomeo, E., Hebditch, K. V., Whittaker, A. C., & Lonergan, L. (2014). Characterising the spatial distribution, frequency and geomorphic controls on landslide occurrence, Molise, Italy. Geomorphology, 226, 148–161. https://doi.org/10.1016/j.geomorph.2014.08.004
dc.relation.referencesBouayad, S., & de Bellefon, M.-P. (2018). Spatial autocorrelation indices. Handbook of Spatial Analysis, Theory and Application with R, 52–70.
dc.relation.referencesBroothaerts, N., Kissi, E., Poesen, J., Van Rompaey, A., Getahun, K., Van Ranst, E., & Diels, J. (2012). Spatial patterns, causes and consequences of landslides in the Gilgel Gibe catchment, SW Ethiopia. Catena, 97, 127–136. https://doi.org/10.1016/j.catena.2012.05.011
dc.relation.referencesBrunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281–298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
dc.relation.referencesBrunsdon, Chris, Fotheringham, S., & Charlton, M. (1998). Geographically Weigthed Regression-modelling spatial non-stationarity. 431–443.
dc.relation.referencesBrunsdon, Chris, Fotheringham, S., & Charlton, M. (2000). Geographically Weighted Regression as a Statistical Model. June 2013, 1–12.
dc.relation.referencesBuma, J., & Dehn, M. (1998). A method for predicting the impact of climate change on slope stability. Environmental Geology, 35(2–3), 190–196. https://doi.org/10.1007/s002540050305
dc.relation.referencesBurnham, K., & Anderson, D. (2004). Model Selection and Multimodel Inference. In K. P. Burnham & D. R. Anderson (Eds.), The Journal of Wildlife Management (Vol. 65, Issue 3). Springer New York. https://doi.org/10.1007/b97636
dc.relation.referencesBurnham, K. P., Anderson, D. R., & Huyvaert, K. P. (2011). AIC model selection and multimodel inference in behavioral ecology: Some background, observations, and comparisons. Behavioral Ecology and Sociobiology, 65(1), 23–35. https://doi.org/10.1007/s00265-010-1029-6
dc.relation.referencesCalcaterra, D., & Parise, M. (2005). Landslide types and their relationships with weathering in a Calabrian basin, southern Italy. Bulletin of Engineering Geology and the Environment, 64(2), 193–207. https://doi.org/10.1007/s10064-004-0262-5
dc.relation.referencesCalcaterra, D., & Parise, M. (2010). Weathering as a predisposing factor to slope movements: An introduction. Geological Society Engineering Geology Special Publication, 23, 1–4. https://doi.org/10.1144/EGSP23.1
dc.relation.referencesCalderon Larrañaga, Y. (2013). Relaciones entre las amenazas naturales por movimientos en masa asociadas a la minería tradicional, con los procesos de urbanización en el municipio de Soacha Cundinamarca [Universidad Nacional de Colombia]. http://www.bdigital.unal.edu.co/45793/
dc.relation.referencesCano-Burgos, B. (2022). Zonificación de amenaza por avenida torrencial para la quebrada Colorada, producto de la rotura de una presa natural formada y sus efectos en el casco urbano del municipio de Paz de Río, Boyacá. Escuela Colombiana de Ingeniería Julio Garavito.
dc.relation.referencesCanuti, P., Garzonio, C. A., & Vannocci, P. (1992). Lateral spreads and landslide hazards to the Northern Appenine. The example of Mt. Fumaiolo (Emilia Romagna) and Chiusi della Verna (Tuscany). International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 29(1), A53. https://doi.org/10.1016/0148-9062(92)91472-h
dc.relation.referencesCardozo, O., García-Palomares, J., & Gutiérrez, J. (2012). Application of geographically weighted regression to the direct forecasting of transit ridership at station-level. Applied Geography, 34, 548–558. https://doi.org/10.1016/j.apgeog.2012.01.005
dc.relation.referencesCarrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V., & Reichenbach, P. (1991). GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms, 16(5), 427–445. https://doi.org/10.1002/esp.3290160505
dc.relation.referencesCarrara, Alberto, Crosta, G., & Frattini, P. (2003). Geomorphological and historical data in assessing landslide hazard. Earth Surface Processes and Landforms, 28(10), 1125–1142. https://doi.org/10.1002/esp.545
dc.relation.referencesCarrara, Alberto, Crosta, G., & Frattini, P. (2008). Comparing models of debris-flow susceptibility in the alpine environment. Geomorphology, 94(3–4), 353–378. https://doi.org/10.1016/j.geomorph.2006.10.033
dc.relation.referencesCartus, A. R., Bodnar, L. M., & Naimi, A. I. (2020). The Impact of Undersampling on the Predictive Performance of Logistic Regression and Machine Learning Algorithms. Epidemiology, 31(5), e42–e44. https://doi.org/10.1097/EDE.0000000000001198
dc.relation.referencesCatani, F., Lagomarsino, D., Segoni, S., & Tofani, V. (2013). Landslide susceptibility estimation by random forests technique: Sensitivity and scaling issues. Natural Hazards and Earth System Sciences, 13(11), 2815–2831. https://doi.org/10.5194/nhess-13-2815-2013
dc.relation.referencesCavanaugh, J. E., & Neath, A. A. (2019). The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements. Wiley Interdisciplinary Reviews: Computational Statistics, 11(3), 1–11. https://doi.org/10.1002/wics.1460
dc.relation.referencesCerri, R. I., Rosolen, V., Reis, F. A. G. V., Filho, A. J. P., Vemado, F., do Carmo Giordano, L., & Gabelini, B. M. (2020). The assessment of soil chemical, physical, and structural properties as landslide predisposing factors in the Serra do Mar mountain range (Caraguatatuba, Brazil). Bulletin of Engineering Geology and the Environment, 79(7), 3307–3320. https://doi.org/10.1007/s10064-020-01791-1
dc.relation.referencesChalkias, C., Kalogirou, S., & Ferentinou, M. (2014). Landslide susceptibility, Peloponnese Peninsula in South Greece. Journal of Maps, 10(2), 211–222. https://doi.org/10.1080/17445647.2014.884022
dc.relation.referencesChalkias, Christos, Kalogirou, S., & Ferentinou, M. (2011). Global and Local statistical modeling for landslide susceptibility assessment , a comparative analysis . Proceedings of the 17th European Colloquium on Quantitative and Theoretical Geography, September, 88–97.
dc.relation.referencesChalkias, Christos, Polykretis, C., Karymbalis, E., Soldati, M., Ghinoi, A., & Ferentinou, M. (2020). Exploring spatial non-stationarity in the relationships between landslide susceptibility and conditioning factors: a local modeling approach using geographically weighted regression. Bulletin of Engineering Geology and the Environment, 79(6), 2799–2814. https://doi.org/10.1007/s10064-020-01733-x
dc.relation.referencesCharlton, M., & Fotheringham, S. (2009). Geographically Weighted Regression - White Paper. National Centre for Geocomputation, 17. https://www.geos.ed.ac.uk/~gisteac/fspat/gwr/gwr_arcgis/GWR_WhitePaper.pdf
dc.relation.referencesChau, K. ., & Chan, J. . (2005a). Regional bias of landslide data in generating susceptibility maps using logistic refression: Case of Hong Kong Island. Landslides, 2(1), 280–290. https://doi.org/10.1007/s10346-015-0576-3
dc.relation.referencesChau, K. T., & Chan, J. E. (2005b). Regional bias of landslide data in generating susceptibility maps using logistic regression: Case of Hong Kong Island. Landslides, 2(4), 280–290. https://doi.org/10.1007/s10346-005-0024-x
dc.relation.referencesChau, K. T., Sze, Y. L., Fung, M. K., Wong, W. Y., Fong, E. L., & Chan, L. C. P. (2004). Landslide hazard analysis for Hong Kong using landslide inventory and GIS. Computers and Geosciences, 30(4), 429–443. https://doi.org/10.1016/j.cageo.2003.08.013
dc.relation.referencesChen, C., He, W., Zhou, H., Xue, Y., & Zhu, M. (2020). A comparative study among machine learning and numerical models for simulating groundwater dynamics in the Heihe River Basin, northwestern China. Scientific Reports, 10(1), 1–13. https://doi.org/10.1038/s41598-020-60698-9
dc.relation.referencesChen, H., Dadson, S., & Chi, Y. G. (2006). Recent rainfall-induced landslides and debris flow in northern Taiwan. Geomorphology, 77(1–2), 112–125. https://doi.org/10.1016/j.geomorph.2006.01.002
dc.relation.referencesChen, L., Guo, Z., Yin, K., Pikha Shrestha, D., & Jin, S. (2019). The influence of land use and land cover change on landslide susceptibility: A case study in Zhushan Town, Xuan’en County (Hubei, China). Natural Hazards and Earth System Sciences, 19(10), 2207–2228. https://doi.org/10.5194/nhess-19-2207-2019
dc.relation.referencesChen, T., Zhu, L., Niu, R. qing, Trinder, C. J., Peng, L., & Lei, T. (2020). Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models. Journal of Mountain Science, 17(3), 670–685. https://doi.org/10.1007/s11629-019-5839-3
dc.relation.referencesChen, W., Pourghasemi, H. R., & Naghibi, S. A. (2018). A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China. Bulletin of Engineering Geology and the Environment, 77(2), 647–664. https://doi.org/10.1007/s10064-017-1010-y
dc.relation.referencesCheng, D., Cui, Y., Su, F., Jia, Y., & Choi, C. E. (2018). The characteristics of the Mocoa compound disaster event, Colombia. Landslides, 15(6), 1223–1232. https://doi.org/10.1007/s10346-018-0969-1
dc.relation.referencesChigira, M., & Kiho, K. (1994). Deep-seated rockslide-avalanches preceded by mass rock creep of sedimentary rocks in the Akaishi Mountains, central Japan. Engineering Geology, 38(3–4), 221–230. https://doi.org/10.1016/0013-7952(94)90039-6
dc.relation.referencesChung, C. J. F., & Fabbri, A. G. (1993). The representation of geoscience information for data integration. Nonrenewable Resources, 2(2), 122–139. https://doi.org/10.1007/BF02272809
dc.relation.referencesChung, C. J. F., & Fabbri, A. G. (2003). Validation of spatial prediction models for landslide hazard mapping. Natural Hazards, 30(3), 451–472. https://doi.org/10.1023/B:NHAZ.0000007172.62651.2b
dc.relation.referencesColegial, J. (1989). Deslizamientos en el área urbana de Socotá Departamento de Boyacá (p. 24). Instituto Nacional de Investigaciones Geológico-Mineras.
dc.relation.referencesComber, A., Brunsdon, C., Charlton, M., Dong, G., Harris, R., Lu, B., Lü, Y., Murakami, D., Nakaya, T., Wang, Y., & Harris, P. (2020). The GWR route map: a guide to the informed application of Geographically Weighted Regression. 1–34. http://arxiv.org/abs/2004.06070
dc.relation.referencesConforti, M., & Ietto, F. (2019). An integrated approach to investigate slope instability affecting infrastructures. Bulletin of Engineering Geology and the Environment, 78(4), 2355–2375. https://doi.org/10.1007/s10064-018-1311-9
dc.relation.referencesConforti, M., & Ietto, F. (2021). Modeling shallow landslide susceptibility and assessment of the relative importance of predisposing factors, through a gis‐based statistical analysis. Geosciences (Switzerland), 11(8), 1–28. https://doi.org/10.3390/geosciences11080333
dc.relation.referencesConforti, M., Pascale, S., Robustelli, G., & Sdao, F. (2014). Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena, 113, 236–250. https://doi.org/10.1016/j.catena.2013.08.006
dc.relation.referencesCongedo, L. (2021). Semi-Automatic Classification Plugin: A Python tool for the download and processing of remote sensing images in QGIS. Journal of Open Source Software, 6(64), 3172. https://doi.org/10.21105/joss.03172
dc.relation.referencesCopas, J. B. (1989). Unweighted Sum of Squares Test for Proportions. Applied Statistics, 38(1), 71. https://doi.org/10.2307/2347682
dc.relation.referencesCoppin, N. J., & Richards, I. (1990). Use of vegetation in civil engineering. https://doi.org/10.5860/CHOICE.28-2750
dc.relation.referencesCorominas, J., van Westen, C., Frattini, P., Cascini, L., Malet, J. P., Fotopoulou, S., Catani, F., Van Den Eeckhaut, M., Mavrouli, O., Agliardi, F., Pitilakis, K., Winter, M. G., Pastor, M., Ferlisi, S., Tofani, V., Hervás, J., & Smith, J. T. (2014). Recommendations for the quantitative analysis of landslide risk. Bulletin of Engineering Geology and the Environment, 73(2), 209–263. https://doi.org/10.1007/s10064-013-0538-8
dc.relation.referencesCORPOBOYACÁ, & UPTC. (2014). Zonificación de áreas de amenaza de origen natural y población en riesgo en el área urbana del municipio de Socotá (p. 190). Universidad Pedagogica y Tecnologica de Colombia. https://www.estadonacion.or.cr/files/biblioteca_virtual/021/politica/Alvarado_Conflicto_Moin
dc.relation.referencesCostanzo, D., Rotigliano, E., Irigaray, C., Jiménez-Perálvarez, J. D., & Chacón, J. (2012). Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: Application to the river Beiro basin (Spain). Natural Hazards and Earth System Science, 12(2), 327–340. https://doi.org/10.5194/nhess-12-327-2012
dc.relation.referencesCostanzo, Dario, Chacón, J., Conoscenti, C., Irigaray, C., & Rotigliano, E. (2014). Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy). Landslides, 11(4), 639–653. https://doi.org/10.1007/s10346-013-0415-3
dc.relation.referencesCrozier, M. (1986). Landslides: Causes, Consequences and Environment (C. Helm (ed.)).
dc.relation.referencesCruden, D. M., & Varnes, D. J. (1996a). Chapter 3 LANDSLIDE TYPES AND PROCESSES. In K. Turner & R. Schuster (Eds.), Landslides: Investigation and Mitigation (Issue Bell 1992, pp. 36–75). Transportation Research Board Special Report 247.
dc.relation.referencesCruden, D. M., & Varnes, D. J. (1996b). Landslide types and processes. Special Report - National Research Council, Transportation Research Board, 247(January 1996), 36–75.
dc.relation.referencesCruden, D., & VanDine, D. (2013). Classification, Description, Causes and Indirect Effects – Canadian Technical Guidelines and Best Practices related to Landslides. Geological Survey of Canada Open File, 7359, 315–322.
dc.relation.referencesD’Amato Avanzi, G., Giannecchini, R., & Puccinelli, A. (2004). The influence of the geological and geomorphological settings on shallow landslides. An example in a temperate climate environment: The June 19, 1996 event in northwestern Tuscany (Italy). Engineering Geology, 73(3–4), 215–228. https://doi.org/10.1016/j.enggeo.2004.01.005
dc.relation.referencesda Silva Henriques, C. (2014). Landslide Susceptibility Evaluation and Validation at a Regional Scale [Universidade de Lisboa]. http://hdl.handle.net/10451/11671
dc.relation.referencesDahal, R. K., Hasegawa, S., Nonomura, A., Yamanaka, M., Masuda, T., & Nishino, K. (2008). GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environmental Geology, 54(2), 311–324. https://doi.org/10.1007/s00254-007-0818-3
dc.relation.referencesDai, F. C., & Lee, C. F. (2002). Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology, 42(3–4), 213–228. https://doi.org/10.1016/S0169-555X(01)00087-3
dc.relation.referencesDai, F. C., Lee, C. F., & Ngai, Y. Y. (2002). Landslide risk assessment and management: An overview. Engineering Geology, 64(1), 65–87. https://doi.org/10.1016/S0013-7952(01)00093-X
dc.relation.referencesDaly, C., Neilson, R. P., & Phillips, D. L. (1994). A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain. Journal of Applied Meteorology, 33(2), 140–158. https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2
dc.relation.referencesDawod, G. M., & Abdel-Aziz, T. M. (2019). Utilization of geographically weighted regression for geoid modelling in Egypt. Journal of Applied Geodesy. https://doi.org/10.1515/jag-2019-0009
dc.relation.referencesDe-Graft, H. (2010). Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of an asymmetric price relationship. Journal of Development and Agricultural Economics, 2(1), 001–006. https://doi.org/10.5897/JDAE.9000032
dc.relation.referencesDe Guidi, G., & Scudero, S. (2013). Landslide susceptibility assessment in the Peloritani Mts. (Sicily, Italy) and clues for tectonic control of relief processes. Natural Hazards and Earth System Science, 13(4), 949–963. https://doi.org/10.5194/nhess-13-949-2013
dc.relation.referencesDelaney, K. B., & Evans, S. G. (2014). The 1997 Mount Munday landslide (British Columbia) and the behaviour of rock avalanches on glacier surfaces. Landslides, 11(6), 1019–1036. https://doi.org/10.1007/s10346-013-0456-7
dc.relation.referencesDeline, P., Alberto, W., Broccolato, M., Hungr, O., Noetzli, J., Ravanel, L., & Tamburini, A. (2011). The December 2008 Crammont rock avalanche, Mont Blanc massif area, Italy. Natural Hazards and Earth System Science, 11(12), 3307–3318. https://doi.org/10.5194/nhess-11-3307-2011
dc.relation.referencesDengo, C. A., & Covey, M. C. (1993). Structure of the Eastern Cordillera of Colombia: implications for trap styles and regional tectonics. In American Association of Petroleum Geologists Bulletin (Vol. 77, Issue 8, pp. 1
dc.relation.referencesDepicker, A., Govers, G., Jacobs, L., Campforts, B., Uwihirwe, J., & Dewitte, O. (2021). Interactions between deforestation, landscape rejuvenation, and shallow landslides in the North Tanganyika-Kivu rift region, Africa. Earth Surface Dynamics, 9(3), 445–462. https://doi.org/10.5194/esurf-9-445-2021
dc.relation.referencesdo Pinho, T. M., & Augusto-Filho, O. (2022). Landslide susceptibility mapping using the infinite slope, SHALSTAB, SINMAP, and TRIGRS models in Serra do Mar, Brazil. Journal of Mountain Science, 19(4), 1018–1036. https://doi.org/10.1007/s11629-021-7057-z
dc.relation.referencesDomencich, T., & McFadden, D. (1975). Statistical Estimation of Choice Probability Functions. In Urban Travel Demand: A Behavioral Analysis (pp. 101–125).
dc.relation.referencesDramis, F., & Sorriso-Valvo, M. (1994). Deep-seated gravitational slope deformations, related landslides and tectonics. Engineering Geology, 38(3–4), 231–243. https://doi.org/10.1016/0013-7952(94)90040-X
dc.relation.referencesDrouillas, Y., Lebourg, T., Zerathe, S., Hippolyte, J. C., Chochon, R., Vidal, M., & Besso, R. (2021). Alpine deep-seated gravitational slope deformation and the Messinian Salinity Crisis. Landslides, 18(2), 539–549. https://doi.org/10.1007/s10346-020-01504-5
dc.relation.referencesDu, G. liang, Zhang, Y. shuang, Iqbal, J., Yang, Z. hua, & Yao, X. (2017). Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China. Journal of Mountain Science, 14(2), 249–268. https://doi.org/10.1007/s11629-016-4126-9
dc.relation.referencesDu, Z., Zhang, B., Hu, H., Bao, J., & Li, W. (2020). Evaluation of Landslide Susceptibility Based on Logistic Regression Model. IOP Conference Series: Earth and Environmental Science, 440(5). https://doi.org/10.1088/1755-1315/440/5/052004
dc.relation.referencesDuman, T. Y., Can, T., Gokceoglu, C., Nefeslioglu, H. A., & Sonmez, H. (2006). Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environmental Geology, 51(2), 241–256. https://doi.org/10.1007/s00254-006-0322-1
dc.relation.referencesEckey, H. F., Kosfeld, R., & Türck, M. (2007). Regional convergence in Germany: A geographically weighted regression approach. Spatial Economic Analysis, 2(1), 45–64. https://doi.org/10.1080/17421770701251905
dc.relation.referencesEhrlich, M., da Costa, D. P., & Silva, R. C. (2018). Behavior of a colluvial slope located in Southeastern Brazil. Landslides, 15(8), 1595–1613. https://doi.org/10.1007/s10346-018-0964-6
dc.relation.referencesEiras, C. G. S., Souza, J. R. G. de, Freitas, R. D. A. de, Barella, C. F., & Pereira, T. M. (2021). Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data. Natural Hazards, 107(2), 1427–1442. https://doi.org/10.1007/s11069-021-04638-4
dc.relation.referencesEpifânio, B., Zêzere, J. L., & Neves, M. (2014). Susceptibility assessment to different types of landslides in the coastal cliffs of Lourinhã (Central Portugal). Journal of Sea Research, 93, 150–159. https://doi.org/10.1016/j.seares.2014.04.006
dc.relation.referencesErener, A. (2009). An approach for landslide risk assessment by using geographic information systems (GIS) and remote sensing (RS) [Middle East Technical University]. http://dx.doi.org/10.1016/B978-0-12-849873-6.00001-7%0Ahttp://saber.ucv.ve/ojs/index.php/rev_venes/article/view/1112%0Ahttps://www.bps.go.id/dynamictable/2018/05/18/1337/persentase-panjang-jalan-tol-yang-beroperasi-menurut-operatornya-2014.html
dc.relation.referencesErener, A., & Düzgün, H. S. B. (2010). Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides, 7(1), 55–68. https://doi.org/10.1007/s10346-009-0188-x
dc.relation.referencesErmini, L., Catani, F., & Casagli, N. (2005). Artificial Neural Networks applied to landslide susceptibility assessment. Geomorphology, 66(1-4 SPEC. ISS.), 327–343. https://doi.org/10.1016/j.geomorph.2004.09.025
dc.relation.referencesErsoz, T., Ersoz, F., & Merdin, D. (2018). Performance Measurement in Business Management with Information Technologies. Baltic Journal of Real Estate Economics and Construction Management, 6(1), 272–284. https://doi.org/10.2478/bjreecm-2018-0020
dc.relation.referencesEsposito, G., Carabella, C., Paglia, G., & Miccadei, E. (2021). Relationships between morphostructural/geological framework and landslide types: Historical landslides in the hilly piedmont area of abruzzo region (central Italy). Land, 10(3). https://doi.org/10.3390/land10030287
dc.relation.referencesEsquivel, J. (1992). Amenaza geológica y ambiental por la mineria del carbón en las veredas de Rucu y Coscativa (Socotá-Boyacá) (p. 31). Instituto de Investigaciones en Geociencias, Minería y Química (Ingeominas).
dc.relation.referencesEstrada, N. K. (2017). Landslide susceptibility prediction in a mountainous catchment: The Naranjo basin, western Guatemala. Liège Université.
dc.relation.referencesEtten, J. Van, Sumner, M., Cheng, J., Baston, D., Bevan, A., Bivand, R., Busetto, L., Canty, M., Fasoli, B., Forrest, D., Golicher, D., Gray, J., Greenberg, J. A., Karney, C., Mattiuzzi, M., & Mosher, S. (2023). Package ‘ raster ’ R topics documented :
dc.relation.referencesEvans, S. G., Tutubalina, O. V., Drobyshev, V. N., Chernomorets, S. S., McDougall, S., Petrakov, D. A., & Hungr, O. (2009). Catastrophic detachment and high-velocity long-runout flow of Kolka Glacier, Caucasus Mountains, Russia in 2002. Geomorphology, 105(3–4), 314–321. https://doi.org/10.1016/j.geomorph.2008.10.008
dc.relation.referencesFawcett, T. (2003). ROC Graphs: Notes and Practical Considerations for Data Mining Researchers ROC Graphs : Notes and Practical Considerations for Data Mining Researchers. HP Invent, 27. https://doi.org/10.1.1.10.9777
dc.relation.referencesFawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010
dc.relation.referencesFell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., & Savage, W. Z. (2008). Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Engineering Geology, 102(3–4), 85–98. https://doi.org/10.1016/j.enggeo.2008.03.022
dc.relation.referencesFeuillet, T., Coquin, J., Mercier, D., Cossart, E., Decaulne, A., Jónsson, H. P., & Sæmundssonthorsteinn, T. (2014). Focusing on the spatial non-stationarity of landslide predisposing factors in northern Iceland: Do paraglacial factors vary over space? Progress in Physical Geography, 38(3), 354–377. https://doi.org/10.1177/0309133314528944
dc.relation.referencesFlach, P. A. (2016). ROC Analysis. In Encyclopedia of Machine Learning and Data Mining (pp. 1–8). Springer US. https://doi.org/10.1007/978-1-4899-7502-7_739-1
dc.relation.referencesFonseca, A., & Torres, M. (1994). Recopilación y evaluación de información geológica de la cuenca carbonífera de Boyacá zona Sogamoso-Jericó sector II para la elaboración del mapa geológico regional (p. 237). “Ecocarbón” Empresa colombiana de carbón.
dc.relation.referencesForte, G., Pirone, M., Santo, A., Nicotera, M. V., & Urciuoli, G. (2019). Triggering and predisposing factors for flow-like landslides in pyroclastic soils: the case study of the Lattari Mts. (southern Italy). Engineering Geology, 257(May), 105137. https://doi.org/10.1016/j.enggeo.2019.05.014
dc.relation.referencesFotheringham, A. S., Charlton, M. E., & Brunsdon, C. (1998). Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and Planning A, 30(11), 1905–1927. https://doi.org/10.1068/a301905
dc.relation.referencesFotheringham, A. Stewart. (2009). “The problem of spatial autocorrelation” and local spatial statistics. Geographical Analysis, 41(4), 398–403. https://doi.org/10.1111/j.1538-4632.2009.00767.x
dc.relation.referencesFotheringham, A. Stewart, Crespo, R., & Yao, J. (2015). Geographical and Temporal Weighted Regression (GTWR). Geographical Analysis, 47(4), 431–452. https://doi.org/10.1111/gean.12071
dc.relation.referencesFotheringham, A. Stewart, & Oshan, T. M. (2016). Geographically weighted regression and multicollinearity: dispelling the myth. Journal of Geographical Systems, 18(4), 303–329. https://doi.org/10.1007/s10109-016-0239-5
dc.relation.referencesFotheringham, S., Brunsdon, C., & Charlton, M. (2002). Geographically Weigthed Regression: the analysis of spatially varying relationships (1era ed.). John Wiley & Sons, LTD.
dc.relation.referencesFox, J. (1991). Regression Diagnostics: An Introduction (SAGE (ed.); Second Edi). SAGE Publications.
dc.relation.referencesFox, J., & Weisberg, S. (2019). An R Companion to Applied Regression. In Thousand Oaks CA: Sage. (Issue September 2012). http://socserv.socsci.mcmaster.ca/jfox/Books/Companion
dc.relation.referencesFujisawa, K., Marcato, G., Nomura, Y., & Pasuto, A. (2010). Management of a typhoon-induced landslide in Otomura (Japan). Geomorphology, 124(3–4), 150–156. https://doi.org/10.1016/j.geomorph.2010.09.027
dc.relation.referencesGarcía-Rodríguez, M. J., Malpica, J. A., Benito, B., & Díaz, M. (2008). Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression. Geomorphology, 95(3–4), 172–191. https://doi.org/10.1016/j.geomorph.2007.06.001
dc.relation.referencesGarcía-Ruiz, J. M., Beguería, S., Arnáez, J., Sanjuán, Y., Lana-Renault, N., Gómez-Villar, A., Álvarez-Martínez, J., & Coba-Pérez, P. (2017). Deforestation induces shallow landsliding in the montane and subalpine belts of the Urbión Mountains, Iberian Range, Northern Spain. Geomorphology, 296, 31–44. https://doi.org/10.1016/j.geomorph.2017.08.016
dc.relation.referencesGeoestudios LTDA. (2006). Cartografía Geológica Cuenca Cordillera Oriental-Sector Soapaga (pp. 1–252). Geoestudios LTDA. https://doi.org/10.1007/978-1-62703-791_4
dc.relation.referencesGlade, T. (2003). Landslide occurrence as a response to land use change: A review of evidence from New Zealand. Catena, 51(3–4), 297–314. https://doi.org/10.1016/S0341-8162(02)00170-4
dc.relation.referencesGlade, T., & Crozier, M. J. (2005). The Nature of Landslide Hazard Impact. In T. Glade, M. Anderson, & M. J. Crozier (Eds.), Landslide Hazard and Risk (pp. 43–74). John Wiley & Sons, Ltd.
dc.relation.referencesGollini, I., Lu, B., Charlton, M., Brunsdon, C., & Harris, P. (2015). Gwmodel: An R package for exploring spatial heterogeneity using geographically weighted models. Journal of Statistical Software, 63(17), 1–50. https://doi.org/10.18637/jss.v063.i17
dc.relation.referencesGómez, H., & Kavzoglu, T. (2005). Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Engineering Geology, 78(1–2), 11–27. https://doi.org/10.1016/j.enggeo.2004.10.004
dc.relation.referencesGoovaerts, P. (1999). Using elevation to aid the geostatistical mapping of rainfall erosivity. Catena, 34(3–4), 227–242. https://doi.org/10.1016/S0341-8162(98)00116-7
dc.relation.referencesGoovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228(1–2), 113–129. https://doi.org/10.1016/S0022-1694(00)00144-X
dc.relation.referencesGorsevski, Pece V., Gessler, P. E., Foltz, R. B., & Elliot, W. J. (2006). Spatial prediction of landslide hazard using logistic regression and ROC analysis. Transactions in GIS, 10(3), 395–415. https://doi.org/10.1111/j.1467-9671.2006.01004.x
dc.relation.referencesGorsevski, Peter V, & Foltz, R. B. (2000). Spatial Prediction of Landslide Hazard Using Discriminant Analysis and GIS GIS in the Rockies 2000 Conference. Analysis, January 2000.
dc.relation.referencesGoyes-Peñafiel, P., & Hernandez-Rojas, A. (2021). Landslide susceptibility index based on the integration of logistic regression and weights of evidence: A case study in Popayan, Colombia. Engineering Geology, 280, 105958. https://doi.org/10.1016/j.enggeo.2020.105958
dc.relation.referencesGuadagno, F. M., Forte, R., Revellino, P., Fiorillo, F., & Focareta, M. (2005). Some aspects of the initiation of debris avalanches in the Campania Region: The role of morphological slope discontinuities and the development of failure. Geomorphology, 66(1-4 SPEC. ISS.), 237–254. https://doi.org/10.1016/j.geomorph.2004.09.024
dc.relation.referencesGuevara, L., Maya, L., Gaya, C., Ciontescu, N., Martin, C., Gutierrez, T., Tovar, D., Cristancho, J., Peralta, B., Cerinza, A., Fandiño, N., Pachón, A., Bello, C., Hincapie, S., & Cortés, A. (2009). Formulación del plan de ordenación y manejo ambiental de la cuenca media del río Chicamocha (p. 82). Corporación Autónoma Regional de Boyacá - CORPOBOYACA. https://www.corpoboyaca.gov.co/cms/wp-content/uploads/2015/11/informe-formulacion-chicamocha.pdf
dc.relation.referencesGuns, M., & Vanacker, V. (2012). Logistic regression applied to natural hazards: Rare event logistic regression with replications. Natural Hazards and Earth System Science, 12(6), 1937–1947. https://doi.org/10.5194/nhess-12-1937-2012
dc.relation.referencesGuthrie, R. H., & Evans, S. G. (2004). Magnitude and frequency of landslides triggered by a storm event, Loughborough Inlet, British Columbia. Natural Hazards and Earth System Science, 4(3), 475–483. https://doi.org/10.5194/nhess-4-475-2004
dc.relation.referencesGuzmán, G. (2016). The Chicamocha River Canyon. In World Geomorphological Landscapes (pp. 73–83). Springer. https://doi.org/10.1007/978-3-319-11800-0_6
dc.relation.referencesGuzzetti, F., Cardinali, M., & Reichenbach, P. (1996). The Influence of Structural Setting and Lithology on Landslide Type and Pattern. Environmental & Engineering Geoscience, II(4), 531–555. https://doi.org/10.2113/gseegeosci.II.4.531
dc.relation.referencesGuzzetti, Fausto, Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 13(6), 1995.
dc.relation.referencesGuzzetti, Fausto, Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., & Chang, K. T. (2012). Landslide inventory maps: New tools for an old problem. Earth-Science Reviews, 112(1–2), 42–66. https://doi.org/10.1016/j.earscirev.2012.02.001
dc.relation.referencesGuzzetti, Fausto, Reichenbach, P., Ardizzone, F., Cardinali, M., & Galli, M. (2006). Estimating the quality of landslide susceptibility models. Geomorphology, 81(1–2), 166–184. https://doi.org/10.1016/j.geomorph.2006.04.007
dc.relation.referencesHallet, D. (1999). Goodness of fit tests in Logistic Regression [University of Toronto]. https://doi.org/10.2307/j.ctvr7f6m7.7
dc.relation.referencesHarris, P., Fotheringham, A. S., Crespo, R., & Charlton, M. (2010). The Use of Geographically Weighted Regression for Spatial Prediction: An Evaluation of Models Using Simulated Data Sets. Mathematical Geosciences, 42(6), 657–680. https://doi.org/10.1007/s11004-010-9284-7
dc.relation.referencesHasekioğulları, G. D., & Ercanoglu, M. (2012). A new approach to use AHP in landslide susceptibility mapping: A case study at Yenice (Karabuk, NW Turkey). Natural Hazards, 63(2), 1157–1179. https://doi.org/10.1007/s11069-012-0218-1
dc.relation.referencesHeckmann, T., Gegg, K., Gegg, A., & Becht, M. (2014). Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows. Natural Hazards and Earth System Sciences, 14(2), 259–278. https://doi.org/10.5194/nhess-14-259-2014
dc.relation.referencesHemmert, G. A. J., Schons, L. M., Wieseke, J., & Schimmelpfennig, H. (2018). Log-likelihood-based Pseudo-R2 in Logistic Regression: Deriving Sample-sensitive Benchmarks. Sociological Methods and Research, 47(3), 507–531. https://doi.org/10.1177/0049124116638107
dc.relation.referencesHenriques, C., Zêzere, J. L., & Marques, F. (2015). The role of the lithological setting on the landslide pattern and distribution. Engineering Geology, 189, 17–31. https://doi.org/10.1016/j.enggeo.2015.01.025
dc.relation.referencesHiemstra, P. H., Pebesma, E. J., Twenhöfel, C. J. W., & Heuvelink, G. B. M. (2009). Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network. Computers and Geosciences, 35(8), 1711–1721. https://doi.org/10.1016/j.cageo.2008.10.011
dc.relation.referencesHighland, L. M., & Bobrowsky, P. (2008). The landslide Handbook - A guide to understanding landslides. In US Geological Survey Circular (Issue 1325). https://doi.org/10.3133/cir1325
dc.relation.referencesHong, H., Pradhan, B., Sameen, M. I., Chen, W., & Xu, C. (2017). Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China). Geomatics, Natural Hazards and Risk, 8(2), 1997–2022. https://doi.org/10.1080/19475705.2017.1403974
dc.relation.referencesHosmer, D., & Lemeshow, S. (2000). Applied Logistic Regression (2nd Editio).
dc.relation.referencesHosmer, D. W., Hosmer, T., Le Cessie, S., & Lemeshow, S. (1997). A comparison of goodness-of-fit tests for the logistic regression model. Statistics in Medicine, 16(9), 965–980. https://doi.org/10.1002/(SICI)1097-0258(19970515)16:9<965::AID-SIM509>3.0.CO;2-O
dc.relation.referencesHosmer, David W., & Hjort, N. L. (2002). Goodness-of-fit processes for logistic regression: simulation results. Statistics in Medicine, 21(18), 2723–2738. https://doi.org/10.1002/sim.1200
dc.relation.referencesHuang, F., Chen, J., Du, Z., Yao, C., Huang, J., & Jiang, Q. (2020). Landslide susceptibility prediction considering regional soil erosion based on machine-learning models. ISPRS International J, 24. https://www.mendeley.com/catalogue/56aaeba4-c7c9-3ede-8400-bea51e1eb8f5/?utm_source=desktop&utm_medium=1.19.8&utm_campaign=open_catalog&userDocumentId=%7B7094c671-c3b6-47ab-a9c3-be9c0e72bfa3%7D
dc.relation.referencesHuang, F., Yao, C., Liu, W., Li, Y., & Liu, X. (2018). Landslide susceptibility assessment in the Nantian area of China: A comparison of frequency ratio model and support vector machine. Geomatics, Natural Hazards and Risk, 9(1), 919–938. https://doi.org/10.1080/19475705.2018.1482963
dc.relation.referencesHuang, P. H. (2017). Asymptotics of AIC, BIC, and RMSEA for Model Selection in Structural Equation Modeling. Psychometrika, 82(2), 407–426. https://doi.org/10.1007/s11336-017-9572-y
dc.relation.referencesHubach, E., & Alvarado, B. (1933). Destrucción de la población de Sativanorte, Boyacá, y el sitio para su reconstrucción. Servicio Geológico Nacional (SGNC).
dc.relation.referencesHughes, K. S., & Schulz, W. (2020). Map depicting susceptibility to landslides triggered by intense rainfall, Puerto Rico. Open-File Report. http://pubs.er.usgs.gov/publication/ofr20201022
dc.relation.referencesHungr, O., Evans, S. G., Bovis, M. J., & Hutchinson, J. N. (2001). A review of the classification of landslides of the flow type. Environmental and Engineering Geoscience, 7(3), 221–238. https://doi.org/10.2113/gseegeosci.7.3.221
dc.relation.referencesHungr, Oldrich. (2005). Classification and terminology. In Debris-flow Hazards and Related Phenomena (First Edit, pp. 9–23). Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-27129-5_2
dc.relation.referencesHungr, Oldrich, Leroueil, S., & Picarelli, L. (2014). The Varnes classification of landslide types, an update. Landslides, 11(2), 167–194. https://doi.org/10.1007/s10346-013-0436-y
dc.relation.referencesIDEAM. (n.d.). Geoportal - IDEAM. Instituto de Hidrología, Meteorología y Estudios Ambientales - IDEAM. http://visor.ideam.gov.co/geovisor/
dc.relation.referencesIglesias, T. (2012). Métodos de Bondad de Ajuste en Regresión Logística. Universidad de Granada.
dc.relation.referencesIlia, I., & Tsangaratos, P. (2016). Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map. Landslides, 13(2), 379–397. https://doi.org/10.1007/s10346-015-0576-3
dc.relation.referencesIncitema. (2015). Estudio de Impacto Ambiental para el área de perforación exploratoria COR-15 (pp. 1–122).
dc.relation.referencesIrfan, T. Y. (1998). Structurally controlled landslides in saprolitic soils in Hong Kong. Geotechnical and Geological Engineering, 16(3), 215–238. https://doi.org/10.1023/A:1008805827178
dc.relation.referencesJaiswal, P., van Westen, C. J., & Jetten, V. (2011). Quantitative assessment of landslide hazard along transportation lines using historical records. Landslides, 8(3), 279–291. https://doi.org/10.1007/s10346-011-0252-1
dc.relation.referencesJakob, M. (2000). The impacts of logging on landslide activity at Clayoquot Sound, British Columbia. Catena, 38(4), 279–300. https://doi.org/10.1016/S0341-8162(99)00078-8
dc.relation.referencesJames, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R (1st ed.). James, Witten, Hasten & Tibshirani.
dc.relation.referencesKanungo, D. P., Arora, M. K., Sarkar, S., & Gupta, R. P. (2006). A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology, 85(3–4), 347–366. https://doi.org/10.1016/j.enggeo.2006.03.004
dc.relation.referencesKavzoglu, T., Kutlug Sahin, E., & Colkesen, I. (2015). Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm. Engineering Geology, 192, 101–112. https://doi.org/10.1016/j.enggeo.2015.04.004
dc.relation.referencesKayastha, Prabin, Dhital, M. R., & De Smedt, F. (2012). Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal. Natural Hazards, 63(2), 479–498. https://doi.org/10.1007/s11069-012-0163-z
dc.relation.referencesKimsey, M. J., Moore, J., & McDaniel, P. (2008). A geographically weighted regression analysis of Douglas-fir site index in north central Idaho. Forest Science, 54(3), 356–366. https://doi.org/10.1093/forestscience/54.3.356
dc.relation.referencesKing, J., Loveday, I., & Schuster, R. L. (1989). The 1985 Bairaman landslide dam and resulting debris flow, Papua New Guinea. Quarterly Journal of Engineering Geology, 22(4), 257–270. https://doi.org/10.1144/gsl.qjeg.1989.022.04.02
dc.relation.referencesKvalseth, T. O. (1985). Cautionary note about r2. American Statistician, 39(4), 279–285. https://doi.org/10.1080/00031305.1985.10479448
dc.relation.referencesLacerda, W. A. (2007). Landslide initiation in saprolite and colluvium in southern Brazil: Field and laboratory observations. Geomorphology, 87(3), 104–119. https://doi.org/10.1016/j.geomorph.2006.03.037
dc.relation.referencesLandínez, A., & Beltrán, D. (2019). Modelo de susceptibilidad a deslizamientos del departamento de Cundinamarca Colombia, fundamentado en las características topográficas del terreno [Universidad de la Salle, Facultad de Ingeniería]. https://ciencia.lasalle.edu.co/ing_civil
dc.relation.referencesLee, S., & Min, K. (2001). Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental Geology, 40(9), 1095–1113. https://doi.org/10.1007/s002540100310
dc.relation.referencesLee, Saro. (2005). Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. International Journal of Remote Sensing, 26(7), 1477–1491. https://doi.org/10.1080/01431160412331331012
dc.relation.referencesLee, Saro. (2007). Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea. Earth Surface Processes and Landforms, 32(14), 2133–2148. https://doi.org/10.1002/esp.1517
dc.relation.referencesLee, Saro, Hong, S. M., & Jung, H. S. (2017). A support vector machine for landslide susceptibility mapping in Gangwon Province, Korea. Sustainability (Switzerland), 9(1), 15–19. https://doi.org/10.3390/su9010048
dc.relation.referencesLee, Saro, & Sambath, T. (2006). Landslide susceptibility mapping in the Damrei Romel area , Cambodia using frequency ratio and logistic regression models. Environmental Geology, April. https://doi.org/10.1007/s00254-006-0256-7
dc.relation.referencesLee, Saro, & Talib, J. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology, 2005, 10. https://doi.org/10.1007/s00254-005-1228-z
dc.relation.referencesLehmann, P., von Ruette, J., & Or, D. (2019). Deforestation Effects on Rainfall-Induced Shallow Landslides: Remote Sensing and Physically-Based Modelling. Water Resources Research, 55(11), 9962–9976. https://doi.org/10.1029/2019WR025233
dc.relation.referencesLepore, C., Kamal, S. A., Shanahan, P., & Bras, R. L. (2012). Rainfall-induced landslide susceptibility zonation of Puerto Rico. Environmental Earth Sciences, 66(6), 1667–1681. https://doi.org/10.1007/s12665-011-0976-1
dc.relation.referencesLewandowska-Gwarda, K. (2018). Geographically weighted regression in the analysis of unemployment in Poland. ISPRS International Journal of Geo-Information, 7(1). https://doi.org/10.3390/ijgi7010017
dc.relation.referencesLi, Y., Liu, X., Han, Z., & Dou, J. (2020). Spatial proximity-based geographically weighted regression model for landslide susceptibility assessment: A case study of Qingchuan area, China. Applied Sciences (Switzerland), 10(3). https://doi.org/10.3390/app10031107
dc.relation.referencesLila, R. (2018). Landslide Hazard Modeling in Ventura and Santa Barbara Counties, California Using Multi-Tiered Geospatial Data Analysis. Texas State University.
dc.relation.referencesLin, G. F., Chang, M. J., Huang, Y. C., & Ho, J. Y. (2017). Assessment of susceptibility to rainfall-induced landslides using improved self-organizing linear output map, support vector machine, and logistic regression. Engineering Geology, 224(April), 62–74. https://doi.org/10.1016/j.enggeo.2017.05.009
dc.relation.referencesLin, J. M., & Billa, L. (2021). Spatial prediction of flood-prone areas using geographically weighted regression. Environmental Advances, 6, 100118. https://doi.org/10.1016/j.envadv.2021.100118
dc.relation.referencesLin, Q., & Wang, Y. (2019). The application of a geograhically weighted logistic regression for earthquake-triggered landslide susceptibility mapping on different geographic scales. In Deep Rock Mechanics: From Research to Engineering (pp. 485–499).
dc.relation.referencesLiu, J. G., & Mason, P. J. (2009). Essential Image Processing and GIS for Remote Sensing (Vol. 5). Wiley. https://doi.org/10.1002/9781118687963
dc.relation.referencesLombardo, L., Cama, M., Conoscenti, C., Märker, M., & Rotigliano, E. (2015). Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: application to the 2009 storm event in Messina (Sicily, southern Italy). Natural Hazards, 79(3), 1621–1648. https://doi.org/10.1007/s11069-015-1915-3
dc.relation.referencesLu, B., Harris, P., Charlton, M., & Brunsdon, C. (2014). The GWmodel R package: Further topics for exploring spatial heterogeneity using geographically weighted models. Geo-Spatial Information Science, 17(2), 85–101. https://doi.org/10.1080/10095020.2014.917453
dc.relation.referencesMallick, J., Singh, R. K., Khan, R. A., Singh, C. K., Kahla, N. Ben, Warrag, E. I., Islam, S., & Rahman, A. (2018). Examining the rainfall–topography relationship using non-stationary modelling technique in semi-arid Aseer region, Saudi Arabia. Arabian Journal of Geosciences, 11(9). https://doi.org/10.1007/s12517-018-3580-9
dc.relation.referencesMartins, T., Vieira, B., Fernandes, N., Oka-Fiori, C., & Montgomery, D. (2017). Application of the SHALSTAB model for the identification of areas susceptible to landslides: Brazilian case studies. Revista de Geomorfologie, 19(1), 136–144. https://doi.org/10.21094/rg.2017.015
dc.relation.referencesMarzban, C. (2004). The ROC curve and the area under it as performance measures. Weather and Forecasting, 19(6), 1106–1114. https://doi.org/10.1175/825.1
dc.relation.referencesMatthias, J., & Hungr, O. (2005). Debris-flow Hazards and Related Phenomena. In Canadian Geotechnical Journal (Vol. 42, Issue 6). Springer Berlin Heidelberg. https://doi.org/10.1007/b138657
dc.relation.referencesMayfield, H. J., Lowry, J. H., Watson, C. H., Kama, M., Nilles, E. J., & Lau, C. L. (2018). Use of geographically weighted logistic regression to quantify spatial variation in the environmental and sociodemographic drivers of leptospirosis in Fiji: a modelling study. The Lancet Planetary Health, 2(5), e223–e232. https://doi.org/10.1016/S2542-5196(18)30066-4
dc.relation.referencesMcFadden, D. (1977). Quantitative methods for analysing travel behaviour ofindividuals: Some recent developments. Cowles Foundation Discussion Paper No. 474, 279–318.
dc.relation.referencesMedina, E., Castro, E., Navarro, S., Sandoval, A., Machuca, S., García, H., Rangel, M., Morales, J., Medina, D., Trejos, G., Valencia, M., Ramírez, K., Rodriguez, E., Barrera, L., & Gamboa, C. (2017). ZONIFICACIÓN DE SUSCEPTIBILIDAD Y AMENAZA POR MOVIMIENTOS EN MASA DE LAS SUBCUENCAS DE LAS QUEBRADAS TARUCA, TARUQUITA, SAN ANTONIO, EL CARMEN Y LOS RÍOS MULATO Y SANGOYACO DEL MUNICIPIO DE MOCOA – PUTUMAYO. Escala 1:25.000 (p. 239). Servicio Geológico Colombiano.
dc.relation.referencesMeinhardt, M., Fink, M., & Tünschel, H. (2015). Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: Comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology, 234, 80–97. https://doi.org/10.1016/j.geomorph.2014.12.042
dc.relation.referencesMennis, J. (2006). Mapping the results of geographically weighted regression. Cartographic Journal, 43(2), 171–179. https://doi.org/10.1179/000870406X114658
dc.relation.referencesMersha, T., & Meten, M. (2020). GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia. Geoenvironmental Disasters, 7(1). https://doi.org/10.1186/s40677-020-00155-x
dc.relation.referencesMeusburger, K., & Alewell, C. (2008). Impacts of anthropogenic and environmental factors on the occurrence of shallow landslides in an alpine catchment (Urseren Valley, Switzerland). Natural Hazards and Earth System Science, 8(3), 509–520. https://doi.org/10.5194/nhess-8-509-2008
dc.relation.referencesMiddya, A. I., & Roy, S. (2021). Geographically varying relationships of COVID-19 mortality with different factors in India. Scientific Reports, 11(1), 1–12. https://doi.org/10.1038/s41598-021-86987-5
dc.relation.referencesMidi, H., Sarkar, S. K., & Rana, S. (2010). Collinearity diagnostics of binary logistic regression model. Journal of Interdisciplinary Mathematics, 13(3), 253–267. https://doi.org/10.1080/09720502.2010.10700699
dc.relation.referencesMiščević, P., Števanić, D., & Štambuk-Cvitanović, N. (2009). Slope instability mechanisms in dipping conglomerates over weathered marls: Bol landslide, Croatia. Environmental Geology, 56(7), 1417–1426. https://doi.org/10.1007/s00254-008-1236-x
dc.relation.referencesMontero, J. (2017). Clasificación de movimiento en masa y su distribución en terrenos geológicos de Colombia. In Clasificación de movimiento en masa y su distribución en terrenos geológicos de Colombia (Primera Ed). Servicio Geológico Colombiano (SGC). https://doi.org/10.32685/9789585978218
dc.relation.referencesMontes, N., Sandoval, A., & Vergara, N. (2000). Base de datos y mapa de fallas activas de Colombia. Ingeominas.
dc.relation.referencesMontgomery, D. R. (1994). Road surface drainage, channel initiation, and slope instability. Water Resources Research, 30(6), 1925–1932. https://doi.org/10.1029/94WR00538
dc.relation.referencesMoore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrological Processes, 5(1), 3–30. https://doi.org/10.1002/hyp.3360050103
dc.relation.referencesMoran, P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1–2), 17–23. https://doi.org/10.1093/biomet/37.1-2.17
dc.relation.referencesMorrissey, K. (2015). Exploring Spatial Variability in the Relationship between Long Term Limiting Illness and Area Level Deprivation at the City Level Using Geographically Weighted Regression. AIMS Public Health, 2(3), 426–440. https://doi.org/10.3934/publichealth.2015.3.426
dc.relation.referencesMousavi, S. Z., Kavian, A., Soleimani, K., Mousavi, S. R., & Shirzadi, A. (2011). GIS-based spatial prediction of landslide susceptibility using logistic regression model. Geomatics, Natural Hazards and Risk, 2(1), 33–50. https://doi.org/10.1080/19475705.2010.532975
dc.relation.referencesMoya, G. (2015). Memoria explicativa del mapa geomorfológico aplicado a movimientos en masa, escala 1:100000. Plancha 152 - Soatá.
dc.relation.referencesMugagga, F., Kakembo, V., & Buyinza, M. (2012). Land use changes on the slopes of Mount Elgon and the implications for the occurrence of landslides. Catena, 90, 39–46. https://doi.org/10.1016/j.catena.2011.11.004
dc.relation.referencesNefeslioglu, H. A., Gokceoglu, C., & Sonmez, H. (2008). An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Engineering Geology, 97(3–4), 171–191. https://doi.org/10.1016/j.enggeo.2008.01.004
dc.relation.referencesNeuhäuser, B. (2014). Landslide Susceptibility and Climate Change Scenarios in Flysch Areas of the Eastern Alps [Julius-Maximilians-Universität Würzburg]. https://opus.bibliothek.uni-wuerzburg.de/opus4-wuerzburg/frontdoor/deliver/index/docId/10858/file/Dissertation_Neuhaeuser.pdf
dc.relation.referencesNguyen, V. V., Pham, B. T., Vu, B. T., Prakash, I., Jha, S., Shahabi, H., Shirzadi, A., Ba, D. N., Kumar, R., Chatterjee, J. M., & Bui, D. T. (2019). Hybrid machine learning approaches for landslide susceptibility modeling. Forests, 10(2). https://doi.org/10.3390/f10020157
dc.relation.referencesOhlmacher, G. C. (2007). Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Engineering Geology, 91(2–4), 117–134. https://doi.org/10.1016/j.enggeo.2007.01.005
dc.relation.referencesOhlmacher, G. C., & Davis, J. C. (2003). Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Engineering Geology, 69(3–4), 331–343. https://doi.org/10.1016/S0013-7952(03)00069-3
dc.relation.referencesOilier, C. D. (2010). Very deep weathering and related landslides. Geological Society Engineering Geology Special Publication, 23, 5–14. https://doi.org/10.1144/EGSP23.2
dc.relation.referencesOmar, L., & Ivrissimtzis, I. (2019). Using theoretical ROC curves for analysing machine learning binary classifiers. Pattern Recognition Letters, 128, 447–451. https://doi.org/10.1016/j.patrec.2019.10.004
dc.relation.referencesOsanai, N., Yamada, T., Hayashi, S. ichiro, Kastura, S., Furuichi, T., Yanai, S., Murakami, Y., Miyazaki, T., Tanioka, Y., Takiguchi, S., & Miyazaki, M. (2019). Characteristics of landslides caused by the 2018 Hokkaido Eastern Iburi Earthquake. Landslides, 16(8), 1517–1528. https://doi.org/10.1007/s10346-019-01206-7
dc.relation.referencesOshan, T. M., Li, Z., Kang, W., Wolf, L. J., & Stewart Fotheringham, A. (2019). MGWR: A python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information, 8(6). https://doi.org/10.3390/ijgi8060269
dc.relation.referencesPachauri, A. K., & Pant, M. (1992). Landslide hazard mapping based on geological attributes. Engineering Geology, 32(1–2), 81–100. https://doi.org/10.1016/0013-7952(92)90020-Y
dc.relation.referencesPacheco, A. (1971). Informe sobre los deslizamientos del municipio de Paz de Río, Boyacá (p. 16). Instituto Nacional de Investigaciones Geológico-Mineras.
dc.relation.referencesPalenzuela-Baena, J. A., Scifoni, S., Marsella, M., De Astis, G., & Irigaray Fernández, C. (2019). Landslide susceptibility mapping on the islands of Vulcano and Lipari (Aeolian Archipelago, Italy), using a multi-classification approach on conditioning factors and a modified GIS matrix method for areas lacking in a landslide inventory. Landslides, 16(5), 969–982. https://doi.org/10.1007/s10346-019-01148-0
dc.relation.referencesPardo, M. E., & Moreno-Arias, R. (2018). El enclave seco del cañón del Chicamocha: biodiversidad y territorio. In Bogotá DC: Fundación Natura.
dc.relation.referencesPark, I., & Lee, S. (2014). Spatial prediction of landslide susceptibility using a decision tree approach: a case study of the Pyeongchang area, Korea. International Journal of Remote Sensing, 35(16), 6089–6112. https://doi.org/10.1080/01431161.2014.943326
dc.relation.referencesPark, S. J., Lee, C. W., Lee, S., & Lee, M. J. (2018). Landslide susceptibility mapping and comparison using decision tree models: A case study of Jumunjin Area, Korea. Remote Sensing, 10(10). https://doi.org/10.3390/rs10101545
dc.relation.referencesPark, S., & Kim, J. (2015). A comparative analysis of landslide susceptibility assessment by using global and spatial regression methods in Inje area, Korea. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 33(6), 579–587. https://doi.org/10.7848/ksgpc.2015.33.6.579
dc.relation.referencesParrish, J. (2013). Factors Affecting Landslides in Forested Terrain. California Geological Survey, January, 1–6.
dc.relation.referencesPebesma, E. J. (2004). Multivariable geostatistics in S: The gstat package. Computers and Geosciences, 30(7), 683–691. https://doi.org/10.1016/j.cageo.2004.03.012
dc.relation.referencesPedrazzini, A., Jaboyedoff, M., Loye, A., & Derron, M. H. (2013). From deep seated slope deformation to rock avalanche: Destabilization and transportation models of the Sierre landslide (Switzerland). Tectonophysics, 605, 149–168. https://doi.org/10.1016/j.tecto.2013.04.016
dc.relation.referencesPercival, J., Tsutsumida, N., Murakami, D., Yoshida, T., & Nakaya, T. (2021). gwpcorMapper: an interactive mapping tool for exploring geographically weighted correlation and partial correla-tion in high-dimensional geospatial datasets. January, 1–18.
dc.relation.referencesPerez, A., & Llinas, R. (1990). El deslizamiento de Coloradales - El Salitre en la zona de Paz de Rio, Boyacá. Geología Colombiana, 17, 169–182.
dc.relation.referencesPersichillo, M. G., Bordoni, M., Cavalli, M., Crema, S., & Meisina, C. (2018). The role of human activities on sediment connectivity of shallow landslides. Catena, 160(September 2017), 261–274. https://doi.org/10.1016/j.catena.2017.09.025
dc.relation.referencesPersichillo, M. G., Bordoni, M., & Meisina, C. (2017). The role of land use changes in the distribution of shallow landslides. Science of the Total Environment, 574, 924–937. https://doi.org/10.1016/j.scitotenv.2016.09.125
dc.relation.referencesPersichillo, M. G., Bordoni, M., Meisina, C., Bartelletti, C., Barsanti, M., Giannecchini, R., D’Amato Avanzi, G., Galanti, Y., Cevasco, A., Brandolini, P., & Galve, J. P. (2017). Shallow landslides susceptibility assessment in different environments. Geomatics, Natural Hazards and Risk, 8(2), 748–771. https://doi.org/10.1080/19475705.2016.1265011
dc.relation.referencesPham, B. T., Jaafari, A., Prakash, I., & Bui, D. T. (2019). A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling. Bulletin of Engineering Geology and the Environment, 78(4), 2865–2886. https://doi.org/10.1007/s10064-018-1281-y
dc.relation.referencesPiacentini, D., Troiani, F., Soldati, M., Notarnicola, C., Savelli, D., Schneiderbauer, S., & Strada, C. (2012). Statistical analysis for assessing shallow-landslide susceptibility in South Tyrol (south-eastern Alps, Italy). Geomorphology, 151–152, 196–206. https://doi.org/10.1016/j.geomorph.2012.02.003
dc.relation.referencesPiciullo, L., Calvello, M., & Cepeda, J. M. (2018). Territorial early warning systems for rainfall-induced landslides. Earth-Science Reviews, 179(February), 228–247. https://doi.org/10.1016/j.earscirev.2018.02.013
dc.relation.referencesPierson, T. C. (2007). Hyperconcentrated flow — transitional process between water flow and debris flow. In Debris-flow Hazards and Related Phenomena (pp. 159–202). Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-27129-5_8
dc.relation.referencesPoudyal, C. P., Chang, C., Oh, H. J., & Lee, S. (2010). Landslide susceptibility maps comparing frequency ratio and artificial neural networks: A case study from the Nepal Himalaya. Environmental Earth Sciences, 61(5), 1049–1064. https://doi.org/10.1007/s12665-009-0426-5
dc.relation.referencesPourghasemi, H. R., Jirandeh, A. G., Pradhan, B., Xu, C., & Gokceoglu, C. (2013). Landslide susceptibility mapping using support vector machine and GIS at the Golestan province, Iran. Journal of Earth System Science, 122(2), 349–369. https://doi.org/10.1007/s12040-013-0282-2
dc.relation.referencesPradhan, B., Chaudhari, A., Adinarayana, J., & Buchroithner, M. F. (2012). Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: A case study at Penang Island, Malaysia. Environmental Monitoring and Assessment, 184(2), 715–727. https://doi.org/10.1007/s10661-011-1996-8
dc.relation.referencesPradhan, B., & Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling and Software, 25(6), 747–759. https://doi.org/10.1016/j.envsoft.2009.10.016
dc.relation.referencesPropastin, P., Kappas, M., & Erasmi, S. (2007). Application of Geographically Weighted Regression to Investigate the Impact of Scale on Prediction Uncertainty by Modelling Relationship between Vegetaion and Climate. International Journal of Spatial Infrastructures Research, 3, 1–22. https://doi.org/10.2902/1725-0463.2008.03.art6
dc.relation.referencesRahmati, O., Haghizadeh, A., Pourghasemi, H. R., & Noormohamadi, F. (2016). Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison. Natural Hazards, 82(2), 1231–1258. https://doi.org/10.1007/s11069-016-2239-7
dc.relation.referencesRefice, A., & Capolongo, D. (2002). Probabilistic modeling of uncertainties in earthquake-induced landslide hazard assessment. Computers and Geosciences, 28(6), 735–749. https://doi.org/10.1016/S0098-3004(01)00104-2
dc.relation.referencesRegmi, A. D., Yoshida, K., Dhital, M. R., & Devkota, K. (2013). Effect of rock weathering, clay mineralogy, and geological structures in the formation of large landslide, a case study from Dumre Besei landslide, Lesser Himalaya Nepal. Landslides, 10(1), 1–13. https://doi.org/10.1007/s10346-011-0311-7
dc.relation.referencesRegmi, N. (2010). Hillslope dynamics in the Paonia-McClure Pass area, Colorado, USA (Issue August) [Texas A&M University]. https://hdl.handle.net/1969.1/ETD-TAMU-2010-08-8573
dc.relation.referencesRegmi, N. R., Giardino, J. R., McDonald, E. V., & Vitek, J. D. (2014). A comparison of logistic regression-based models of susceptibility to landslides in western Colorado, USA. Landslides, 11(2), 247–262. https://doi.org/10.1007/s10346-012-0380-2
dc.relation.referencesRegmi, N. R., Giardino, J. R., & Vitek, J. D. (2014). Characteristics of landslides in western Colorado, USA. Landslides, 11(4), 589–603. https://doi.org/10.1007/s10346-013-0412-6
dc.relation.referencesReichenbach, P., Busca, C., Mondini, A. C., & Rossi, M. (2014). The Influence of Land Use Change on Landslide Susceptibility Zonation: The Briga Catchment Test Site (Messina, Italy). Environmental Management, 54(6), 1372–1384. https://doi.org/10.1007/s00267-014-0357-0
dc.relation.referencesReyes, I. (1983). Tectónica gravitacional en la Cordillera Oriental al este de la Falla de Boyacá (Departamento de Boyacá). In Boletín de Geología, Universidad Industrial de Santander (Vol. 16, Issue 30, pp. 103–117).
dc.relation.referencesReyes, Italo. (1984). Geología de la región de Duitama - Sogamoso - Paz del Río (Departamento de Boyacá). Universidad Pedagogica y Tecnologica de Colombia.
dc.relation.referencesRiley, S. J., DeGloria, S. D., & Elliot, R. (1999). A Terrain Ruggedness Index that Qauntifies Topographic Heterogeneity. Intermountain Journal of Sciences, 5(1–4), 23–27.
dc.relation.referencesRincón, L. C. B. (2019). Elaboración de un inventario de movimientos en masa mediante técnicas geomáticas en el municipio de Villeta Cundinamarca. In 2019 (Issue 8). https://doi.org/10.1088/1751-8113/44/8/085201
dc.relation.referencesRizopoulos, D. (2022). Package ‘ bootStepAIC .’ https://cran.r-project.org/web/packages/bootStepAIC/bootStepAIC.pdf
dc.relation.referencesRobin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12(1), 77. https://doi.org/10.1186/1471-2105-12-77
dc.relation.referencesRoccati, A., Paliaga, G., Luino, F., Faccini, F., & Turconi, L. (2021). GIS-Based Landslide Susceptibility Mapping for Land Use Planning and Risk Assessment. Land, 10(2), 162. https://doi.org/10.3390/land10020162
dc.relation.referencesRodrigues, M., de la Riva, J., & Fotheringham, S. (2014). Modeling the spatial variation of the explanatory factors of human-caused wildfires in Spain using geographically weightedlogistic regression. Applied Geography, 48, 52–63. https://doi.org/10.1016/j.apgeog.2014.01.011
dc.relation.referencesRodríguez, A. (1987). Estudio geológico y mapa de riesgos quebrada La Chapa área de los municipios de Socha, Tasco y Paz de Río Depto. de Boyacá (p. 38). Instituto Nacional de Investigaciones Geológico-Mineras.
dc.relation.referencesRomer, C., & Ferentinou, M. (2016). Shallow landslide susceptibility assessment in a semiarid environment - A Quaternary catchment of KwaZulu-Natal, South Africa. Engineering Geology, 201, 29–44. https://doi.org/10.1016/j.enggeo.2015.12.013
dc.relation.referencesRong, G., Alu, S., Li, K., Su, Y., Zhang, J., Zhang, Y., & Li, T. (2020). Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models—A Case Study of Shuicheng County, China. Water, 12(11), 3066. https://doi.org/10.3390/w12113066
dc.relation.referencesRozos, D., Bathrellos, G. D., & Skillodimou, H. D. (2011). Comparison of the implementation of rock engineering system and analytic hierarchy process methods, upon landslide susceptibility mapping, using GIS: A case study from the Eastern Achaia County of Peloponnesus, GREECE. Environmental Earth Sciences, 63(1), 49–63. https://doi.org/10.1007/s12665-010-0687-z
dc.relation.referencesSahin, E. (2020). Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping. Geocarto International, 0(0), 000. https://doi.org/10.1080/10106049.2020.1831623
dc.relation.referencesSahin, E., & Colkesen, I. (2021). Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping. Geocarto International, 36(11), 1253–1275. https://doi.org/10.1080/10106049.2019.1641560
dc.relation.referencesSahin, E. K. (2020). Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping. Geocarto International, 0(0), 000. https://doi.org/10.1080/10106049.2020.1831623
dc.relation.referencesSajinkumar, K. S., Anbazhagan, S., Pradeepkumar, A. P., & Rani, V. R. (2011). Weathering and landslide occurrences in parts of Western Ghats, Kerala. Journal of the Geological Society of India, 78(3), 249–257. https://doi.org/10.1007/s12594-011-0089-1
dc.relation.referencesSalas, F. (2018). Elaboración de mapas de amenaza por procesos de remoción en masa a partir de la modelación de respuesta hidrológica en cuencas [Universidad Nacional de Colombia, Facultad de Ingeniería]. http://bdigital.unal.edu.co/70672/
dc.relation.referencesSalazar, L. (2018). Susceptibilidad y amenaza a los movimientos en masa de suelos de laderas en zonas cafeteras colombianas. Universidad Nacional de Colombia, Sede Palmira, Facultad de Ciencias Agropecuarias.
dc.relation.referencesSangelantoni, L., Gioia, E., & Marincioni, F. (2018). Impact of climate change on landslides frequency: the Esino river basin case study (Central Italy). In Natural Hazards (Vol. 93, Issue 2). Springer Netherlands. https://doi.org/10.1007/s11069-018-3328-6
dc.relation.referencesSantacana, N. (2001). Análisis de la susceptibilidad del terreno a la formación de deslizamientos superficiales y grandes deslizamientos mediante el uso de sistemas de información geográfica. Aplicación a la cuenca alta del río Llobregat. [Universitat Politècnica de Catalunya]. https://www.tesisenred.net/handle/10803/6213#page=1
dc.relation.referencesSantacana, N., Baeza, B., Corominas, J., De Paz, A., & Marturiá, J. (2003). A GIS-based multivariate statistical analysis for shallow landslide susceptibility mapping in La Pobla de Lillet Area (Eastern Pyrenees, Spain). Natural Hazards, 30(3), 281–295. https://doi.org/10.1023/B:NHAZ.0000007169.28860.80
dc.relation.referencesSasaki, Y., Fujii, A., & Asai, K. (2000). Soil creep process and its role in debris slide generation-field measurements on the north side of Tsukuba Mountain in Japan. Developments in Geotechnical Engineering, 84(C), 199–219. https://doi.org/10.1016/S0165-1250(00)80017-6
dc.relation.referencesSchicker, R., & Moon, V. (2012). Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale. Geomorphology, 161–162, 40–57. https://doi.org/10.1016/j.geomorph.2012.03.036
dc.relation.referencesSGC. (2015). Memoria explicativa de la zonificación de la susceptibilidad y la amenaza relativa por movimientos en masa escala 1:100000, Plancha 152 - Soatá, Departamento de Boyacá. (p. 46). Servicio Geológico Colombiano (SGC).
dc.relation.referencesSGC, & IDEAM. (2016). Memoria explicativa de la zonificación de susceptibilidad y amenaza relativa por movimientos en masa escala 1:100.000 Plancha 172 – Paz de Río (p. 53). Servicio Geológico Colombiano (SGC) e Instituto de Hidrología, Metereología y Estudios Ambientales (IDEAM).
dc.relation.referencesSidle, R. C., Pearce, A. J., & O’Loughlin, C. L. (1985). Hillslope Stability and Land Use (Vol. 11, Issue 1). American Geophysical Union. https://doi.org/10.1029/WM011
dc.relation.referencesSidle, R. C., & Ziegler, A. D. (2012). The dilemma of mountain roads. Nature Geoscience, 5(7), 437–438. https://doi.org/10.1038/ngeo1512
dc.relation.referencesSignorell, A., Abo, K., Anderegg, N., Aragon, T., Arachechige, C., Arppe, A., Barton, K., & Bolker, B. (2023). Package “DescTools.” https://cran.r-project.org/web/packages/DescTools/index.html
dc.relation.referencesSing, T., Sander, O., Beerenwinkel, N., & Lengauer, T. (2005). ROCR: Visualizing classifier performance in R. Bioinformatics, 21(20), 3940–3941. https://doi.org/10.1093/bioinformatics/bti623
dc.relation.referencesSingh, N. S., Gupta, S. K., Dubey, C. S., & Shukla, D. P. (2021). An Ordinal Scale Weighting Approach for Susceptibility Mapping Around Tehri Dam, Uttarakhand, India (Vol. 2). https://doi.org/10.1007/978-3-030-60227-7_18
dc.relation.referencesSkilodimou, H. D., Bathrellos, G. D., Koskeridou, E., Soukis, K., & Rozos, D. (2018). Physical and anthropogenic factors related to landslide activity in the northern Peloponnese, Greece. Land, 7(3). https://doi.org/10.3390/land7030085
dc.relation.referencesSoeters, R., & Van Westen, C. J. (1996). Slope instability recognition, analysis, and zonation. Special Report - National Research Council, Transportation Research Board, 247(January 1996), 129–177.
dc.relation.referencesSonoda, M., & Kurashige, Y. (2017). Characteristics of surface soil creep on a forest slope in Japan. Geomorphology, 288, 1–11. https://doi.org/10.1016/j.geomorph.2017.03.006
dc.relation.referencesSørensen, R., Zinko, U., & Seibert, J. (2006). On the calculation of the topographic wetness index: Evaluation of different methods based on field observations. Hydrology and Earth System Sciences, 10(1), 101–112. https://doi.org/10.5194/hess-10-101-2006
dc.relation.referencesSoto, J. (2018). Análisis de la peligrosidad frente a los movimientos de ladera en la Cuenca de Loja (Ecuador). [Universidad de Granada]. http://hdl.handle.net/10481/51128
dc.relation.referencesSun, D., Wen, H., Zhang, Y., & Xue, M. (2021). An optimal sample selection-based logistic regression model of slope physical resistance against rainfall-induced landslide. Natural Hazards, 105(2), 1255–1279. https://doi.org/10.1007/s11069-020-04353-6
dc.relation.referencesSun, X., Chen, J., Bao, Y., Han, X., Zhan, J., & Peng, W. (2018). Landslide susceptibility mapping using logistic regression analysis along the Jinsha river and its tributaries close to Derong and Deqin County, southwestern China. ISPRS International Journal of Geo-Information, 7(11), 1–29. https://doi.org/10.3390/ijgi7110438
dc.relation.referencesSüzen, M. L., & Doyuran, V. (2004). Data driven bivariate landslide susceptibility assessment using geographical information systems: A method and application to Asarsuyu catchment, Turkey. Engineering Geology, 71(3–4), 303–321. https://doi.org/10.1016/S0013-7952(03)00143-1
dc.relation.referencesSüzen, M. L., & Kaya, B. Ş. (2012). Evaluation of environmental parameters in logistic regression models for landslide susceptibility mapping. International Journal of Digital Earth, 5(4), 338–355. https://doi.org/10.1080/17538947.2011.586443
dc.relation.referencesSwanston, D. N. (1971). Slope Stability Problems Associated with Timber Harvesting in Mountainous Regions of the Western United States. USDA Forest Service General Technical Report PNW-21. https://doi.org/10.1.1.486.6343
dc.relation.referencesSwets, J. (1988). Measuring the accuracy of diagnostic systems. Science, 240(4857), 1285–1293. https://doi.org/10.1126/science.3287615
dc.relation.referencesTaalab, K., Cheng, T., & Zhang, Y. (2018). Mapping landslide susceptibility and types using Random Forest. Big Earth Data, 2(2), 159–178. https://doi.org/10.1080/20964471.2018.1472392
dc.relation.referencesTaboada, A., Dimate, C., & Fuenzalida, A. (1998). Sismotectónica de Colombia: deformación continental activa y subducción. Journal of Volcanology and Geothermal Research, 10, 110–147.
dc.relation.referencesTanyu, B. F., Abbaspour, A., Alimohammadlou, Y., & Tecuci, G. (2021). Landslide susceptibility analyses using Random Forest, C4.5, and C5.0 with balanced and unbalanced datasets. Catena, 203(April 2020), 105355. https://doi.org/10.1016/j.catena.2021.105355
dc.relation.referencesTehrany, M. S., Pradhan, B., & Jebur, M. N. (2013). Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology, 504, 69–79. https://doi.org/10.1016/j.jhydrol.2013.09.034
dc.relation.referencesTemme, A. J. A. M. (2021). Relations Between Soil Development and Landslides. In Geophysical Monograph Series (pp. 177–185). https://doi.org/10.1002/9781119563952.ch9
dc.relation.referencesTerlien, M. T. J. (1998). The determination of statistical and deterministic hydrological landslide-triggering thresholds. Environmental Geology, 35(2–3), 124–130. https://doi.org/10.1007/s002540050299
dc.relation.referencesTien Bui, D., Pradhan, B., Lofman, O., Revhaug, I., & Dick, O. B. (2012). Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg-Marquardt and Bayesian regularized neural networks. Geomorphology, 171–172, 12–29. https://doi.org/10.1016/j.geomorph.2012.04.023
dc.relation.referencesToro, J. (1990). The termination of the bucaramanga fault in the Cordillera Oriental, Colombia [The University of Arizona]. http://hdl.handle.net/10150/558140
dc.relation.referencesUlloa, C., Guerra, A., & Escovar, R. (2003). Geología de la Plancha 172 Paz de Río (p. 1). Servicio Geológico Colombiano (SGC).
dc.relation.referencesUlloa, C., Rodriguez, E., & Rodríguez, G. (2003). Memoria explicativa Plancha 172 Paz de Río.
dc.relation.referencesUPTC. (2015). Determinación de zonas de amenaza y escenarios de riesgo por deslizamiento en el municipio de Jericó, departamento de Boyacá. In Universidad Pedagógica y Tecnológico de Colombia.
dc.relation.referencesValencia Ortiz, J. A., & Martínez-Graña, A. M. (2023). Morphometric Evaluation and Its Incidence in the Mass Movements Present in the Chicamocha Canyon, Colombia. Sustainability, 15(2), 1140. https://doi.org/10.3390/su15021140
dc.relation.referencesVan Asch, T. W. J., Buma, J., & Van Beek, L. P. H. (1999). A view on some hydrological triggering systems in landslides. Geomorphology, 30(1–2), 25–32. https://doi.org/10.1016/S0169-555X(99)00042-2
dc.relation.referencesvan Beek, L., & van Asch, T. (2004). Regional assessment of the effects of land-use change on landslide hazard by means of physically based modelling. Natural Hazards, 31(1), 289–304. https://doi.org/10.1023/B:NHAZ.0000020267.39691.39
dc.relation.referencesVan Den Eeckhaut, M., Hervás, J., Jaedicke, C., Malet, J. P., Montanarella, L., & Nadim, F. (2012). Statistical modelling of Europe-wide landslide susceptibility using limited landslide inventory data. Landslides, 9(3), 357–369. https://doi.org/10.1007/s10346-011-0299-z
dc.relation.referencesVan Den Eeckhaut, M., Marre, A., & Poesen, J. (2010). Comparison of two landslide susceptibility assessments in the Champagne-Ardenne region (France). Geomorphology, 115(1–2), 141–155. https://doi.org/10.1016/j.geomorph.2009.09.042
dc.relation.referencesVan Den Eeckhaut, M., Vanwalleghem, T., Poesen, J., Govers, G., Verstraeten, G., & Vandekerckhove, L. (2006). Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium). Geomorphology, 76(3–4), 392–410. https://doi.org/10.1016/j.geomorph.2005.12.003
dc.relation.referencesvan Westen, C. J., & Terlien, M. T. J. (1996). An approach towards deterministic landslide hazard analysis in GIS. A case study from Manizales (Colombia). Earth Surface Processes and Landforms, 21(9), 853–868. https://doi.org/10.1002/(SICI)1096-9837(199609)21:9<853::AID-ESP676>3.0.CO;2-C
dc.relation.referencesVan Westen, C. (1997). Statistical landslide hazard analysis. ILWIS 2.1 for Windows application guide. ITC Publication, 2, 73–84. http://www.adpc.net/casita/Case_studies/Landslide hazard assessment/Statistical landslide susceptibility assessmen landslide index method CS Chinchina Colombia/Statistical_landslide_susceptibility_analysis.pdf
dc.relation.referencesvan Westen, Cees. (1993). Application of Geographic information systems to Landslide Hazard Zonation.
dc.relation.referencesvan Westen, Cees. (1994). GIS in landslide hazard zonation: a review, with examples from the Andes Colombia. Mountain Environment and Geographic Information Systems, May, 135–165.
dc.relation.referencesVargas, R., Arias, A., Jaramillo, L., & Tellez, N. (1987). Plancha 152 - Soatá (p. 1). Instituto Nacional de Investigaciones Geológico-Mineras.
dc.relation.referencesVarnes, D. (1958). Landslide types and processes. In Landslides and Engineering Practice (Vol. 29, pp. 20–47).
dc.relation.referencesVarnes, D. (1978). Slope movement types and processes. Landslides: Analysis and Control. Transportation Research Board Special Report 176, 11–33.
dc.relation.referencesVelandia, F. (2005). Interpretación De Transcurrencia De Las Fallas Soapaga Y Boyacá a Partir De Imágenes Landsat Tm. Boletín de Geología, 27(1), 81–94.
dc.relation.referencesVijith, H., & Madhu, G. (2008). Estimating potential landslide sites of an upland sub-watershed in Western Ghat’s of Kerala (India) through frequency ratio and GIS. Environmental Geology, 55(7), 1397–1405. https://doi.org/10.1007/s00254-007-1090-2
dc.relation.referencesWang, Gonghui, Sassa, K., & Fukuoka, H. (2003). Downslope volume enlargement of a debris slide-debris flow in the 1999 Hiroshima, Japan, rainstorm. Engineering Geology, 69(3–4), 309–330. https://doi.org/10.1016/S0013-7952(02)00289-2
dc.relation.referencesWang, Guirong, Chen, X., & Chen, W. (2020). Spatial prediction of landslide susceptibility based on GIS and discriminant functions. ISPRS International Journal of Geo-Information, 9(3). https://doi.org/10.3390/ijgi9030144
dc.relation.referencesWang, J., & Xiao, L. (2014). Landslide susceptibility assessment based on GIS and weighted information value: A case study of Wanzhou district, Three Gorges Reservoir. Chinese Journal of Rock Mechanics and Engineering, 33(4), 13.
dc.relation.referencesWheeler, D., & Tiefelsdorf, M. (2005). Multicollinearity and correlation among local regression coefficients in geographically weighted regression. Journal of Geographical Systems, 7(2), 161–187. https://doi.org/10.1007/s10109-005-0155-6
dc.relation.referencesWieczorek, G. F. (1996). Chapter 4:Landslide Triggering Mechanisms. In R. L. Schuster & A. K. Turner (Eds.), Landslides: Investigation and Mitigation (pp. 76–90). Transportation Research Board: National Research Council.
dc.relation.referencesWilson, J. P., & Gallant, J. C. (2000). Terrain analysis: principles and applications (J. P. Wilson & J. C. Gallant (eds.)). Wiley. https://www.wiley.com/en-ie/Terrain+Analysis:+Principles+and+Applications-p-9780471321880
dc.relation.referencesWilson, M. F. J., O’Connell, B., Brown, C., Guinan, J. C., & Grehan, A. J. (2007). Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope. In Marine Geodesy (Vol. 30, Issues 1–2). https://doi.org/10.1080/01490410701295962
dc.relation.referencesWu, C., & Qiao, J. (2009). Relationship between landslides and lithology in the Three Gorges Reservoir area based on GIS and information value model. Frontiers of Forestry in China, 4(2), 165–170. https://doi.org/10.1007/s11461-009-0030-6
dc.relation.referencesXian-Qin Hu, & Cruden, D. M. (1992). Rock mass movements across bedding in Kananaskis Country, Alberta. Canadian Geotechnical Journal, 29(4), 675–685. https://doi.org/10.1139/t92-074
dc.relation.referencesXin, P., Liu, Z., Wu, S. ren, Liang, C., & Lin, C. (2018). Rotational–translational landslides in the neogene basins at the northeast margin of the Tibetan Plateau. Engineering Geology, 244(August), 107–115. https://doi.org/10.1016/j.enggeo.2018.07.024
dc.relation.referencesXu, C., Dai, F., Xu, X., & Lee, Y. H. (2012). GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology, 145–146, 70–80. https://doi.org/10.1016/j.geomorph.2011.12.040
dc.relation.referencesXu, Q., Fan, X., Huang, R., Yin, Y., Hou, S., Dong, X., & Tang, M. (2010). A catastrophic rockslide-debris flow in Wulong, Chongqing, China in 2009: Background, characterization, and causes. Landslides, 7(1), 75–87. https://doi.org/10.1007/s10346-009-0179-y
dc.relation.referencesYalcin, A., Reis, S., Aydinoglu, A. C., & Yomralioglu, T. (2011). A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena, 85(3), 274–287. https://doi.org/10.1016/j.catena.2011.01.014
dc.relation.referencesYalcin, Ali. (2008). GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations. Catena, 72(1), 1–12. https://doi.org/10.1016/j.catena.2007.01.003
dc.relation.referencesYoon, S. S., & Bae, D. H. (2013). Optimal rainfall estimation by considering elevation in the Han River Basin, South Korea. Journal of Applied Meteorology and Climatology, 52(4), 802–818. https://doi.org/10.1175/JAMC-D-11-0147.1
dc.relation.referencesZêzere, J. L., Pereira, S., Melo, R., Oliveira, S. C., & Garcia, R. A. C. (2017). Mapping landslide susceptibility using data-driven methods. Science of the Total Environment, 589, 250–267. https://doi.org/10.1016/j.scitotenv.2017.02.188
dc.relation.referencesZêzere, José Luís, De Brum Ferreira, A., & Rodrigues, M. L. (1999). The role of conditioning and triggering factors in the occurrence of landslides: A case study in the area north of Lisbon (Portugal). Geomorphology, 30(1–2), 133–146. https://doi.org/10.1016/S0169-555X(99)00050-1
dc.relation.referencesZhang, D., Ren, N., & Hou, X. (2018). An improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mapping. Geoscientific Model Development, 11(6), 2525–2539. https://doi.org/10.5194/gmd-11-2525-2018
dc.relation.referencesZhu, L., & Huang, J. F. (2006). GIS-based logistic regression method for landslide susceptibility mapping in regional scale. Journal of Zhejiang University: Science, 7(12), 2007–2017. https://doi.org/10.1631/jzus.2006.A2007
dc.relation.referencesZizioli, D., Meisina, C., Valentino, R., & Montrasio, L. (2013). Comparison between different approaches to modeling shallow landslide susceptibility: A case history in Oltrepo Pavese, Northern Italy. Natural Hazards and Earth System Sciences, 13(3), 559–573. https://doi.org/10.5194/nhess-13-559-2013
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.proposalSusceptibilidad
dc.subject.proposalMovimiento en masa
dc.subject.proposalGeomorfología
dc.subject.proposalAmenazas naturales
dc.subject.proposalDeslizamientos
dc.subject.proposalSusceptibility
dc.subject.proposalMass movement
dc.subject.proposalGeomorphology
dc.subject.proposalNatural hazards
dc.subject.proposalLandslides
dc.title.translatedEstimation of shallow landslide susceptibility by means of a local spatial regression analysis. Application to a section of the middle Chicamocha River basin
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.redcolhttp://purl.org/redcol/resource_type/TM
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2
dcterms.audience.professionaldevelopmentInvestigadores
dcterms.audience.professionaldevelopmentPúblico general
dc.subject.wikidataCorrimiento de tierra
dc.subject.wikidatalandslide
dc.subject.wikidataAnálisis de la regresión
dc.subject.wikidataregression analysis
dc.subject.wikidataRiesgo natural
dc.subject.wikidatanatural risk


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