| Autor: |
Bruzón AG; Department of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, 28933 Móstoles, Spain., Arrogante-Funes P; Department of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, 28933 Móstoles, Spain., Arrogante-Funes F; Grupo de Investigación en Teledetección Ambiental, Unidad Docente de Geografía, Geología y Medio Ambiente, Área de Geografía, Universidad de Alcalá, Filosofía y Letras, 28801 Alcalá de Henares, Spain., Martín-González F; Área de Geología, ESCET, Universidad Rey Juan Carlos, 28933 Móstoles, Spain., Novillo CJ; Research Group on Technologies for Landscape Analysis and Diagnosis (TADAT), Department of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, 28933 Móstoles, Spain., Fernández RR; Data Science Laboratory, Rey Juan Carlos University, 28933 Móstoles, Spain., Vázquez-Jiménez R; Cuerpo Académico UAGro CA-93 Riesgos Naturales y Geotecnología, FI, Universidad Autónoma de Guerrero, Chilpancingo 39070, Mexico., Alarcón-Paredes A; Cuerpo Académico UAGro CA-178 Desarrollo Tecnológico Aplicado, Universidad Autónoma de Guerrero, Chilpancingo 39070, Mexico., Alonso-Silverio GA; Cuerpo Académico UAGro CA-178 Desarrollo Tecnológico Aplicado, Universidad Autónoma de Guerrero, Chilpancingo 39070, Mexico., Cantu-Ramirez CA; Ingeniería para la Innovación y Desarrollo Tecnológico, Unidad Académica de Ingeniería Dependiente, Universidad Autónoma de Guerrero, Chilpancingo 39070, Mexico., Ramos-Bernal RN; Cuerpo Académico UAGro CA-93 Riesgos Naturales y Geotecnología, FI, Universidad Autónoma de Guerrero, Chilpancingo 39070, Mexico. |
| Abstrakt: |
The risks associated with landslides are increasing the personal losses and material damages in more and more areas of the world. These natural disasters are related to geological and extreme meteorological phenomena (e.g., earthquakes, hurricanes) occurring in regions that have already suffered similar previous natural catastrophes. Therefore, to effectively mitigate the landslide risks, new methodologies must better identify and understand all these landslide hazards through proper management. Within these methodologies, those based on assessing the landslide susceptibility increase the predictability of the areas where one of these disasters is most likely to occur. In the last years, much research has used machine learning algorithms to assess susceptibility using different sources of information, such as remote sensing data, spatial databases, or geological catalogues. This study presents the first attempt to develop a methodology based on an automatic machine learning (AutoML) framework. These frameworks are intended to facilitate the development of machine learning models, with the aim to enable researchers focus on data analysis. The area to test/validate this study is the center and southern region of Guerrero (Mexico), where we compare the performance of 16 machine learning algorithms. The best result achieved is the extra trees with an area under the curve (AUC) of 0.983. This methodology yields better results than other similar methods because using an AutoML framework allows to focus on the treatment of the data, to better understand input variables and to acquire greater knowledge about the processes involved in the landslides. |
