Metodologías para la evaluación de la amenaza por movimientos en masa como parte de los estudios básico de amenaza: caso de estudio municipio de Andes, Antioquia, Colombia

Abstract

Los estudios básicos de susceptibilidad y amenaza por la ocurrencia de movimientos en masa son un elemento fundamental para la actualización de los planes de ordenamiento de los municipios del territorio colombiano. Dado lo anterior, la Ley 1523 de 2012 establece la política marco, y el Decreto 1807 de 2014, compilado en el 1077 de 2015, establece los lineamientos técnicos que tales estudios deben seguir y las condiciones mínimas que se deben cumplir. Por este motivo, se realizó una selección de algunas metodologías reconocidas en la literatura, que, al ser adecuadas y validadas según las condiciones propias de cada municipalidad, pueden ser utilizadas para la realización de tales estudios, sean tanto para el área rural y para suelo urbano y de expansión, como para cada uno de los factores que pueden detonar estos eventos.

Landslide Susceptibility Analysis on the Vicinity of Bogotá-Villavicencio Road (Eastern Cordillera of the Colombian Andes)

“…Regarding factor analysis, the number of factors to be used in the models can be very high, especially in machine learning models [33], where it is common not to perform factor selection and allow the algorithms to fit the models. In this work, 12 variables have been used, which more or less coincide with those used in previous studies on susceptibility modelling, both globally [35,36,38,[40][41][42]90,97,103,104,106,107] and in Colombian Andes [31,[44][45][46][47][48][49][50][51][52][53]. Among them, the ones derived from the DEM stand out, which are related to the spatial distribution of important parameters such as slope, morphology, soil moisture or flow direction [51,117,118]; those related to geology and the geomechanical characteristics of materials [119] or those related to land use and land cover [81].…”

“…Different studies have been conducted in the Colombian Andes, using all types of methods from index-based methods such as frequency ratio or weight of evidence [44][45][46][47][48]; statistical methods such as logistic regression [31,46,[48][49][50][51]; machine learning methods such as random forest [46], graded boosted regression trees, GBRT [46] or multivariate adaptive regression [31] and simple [46,52] or convolutional neural networks [53]. The methods have been applied to different areas such as Capitanejo in the NE of the country [52], Medellín and the department of Antioquia to the NW [47][48][49], Caldas [44], Cauca [45,50], Boyacá [52], Bogotá and Cundinamarca [46,51] and Mocoa in the south [31]. Most of these studies, not only in Colombia but also throughout the world, do not take into account the landslide typology, which produces less precision and noise in the models.…”