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Landslide Susceptibility Assessment Using an AutoML Framework
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 sim...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8535781/ https://www.ncbi.nlm.nih.gov/pubmed/34682717 http://dx.doi.org/10.3390/ijerph182010971 |
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author | Bruzón, Adrián G. Arrogante-Funes, Patricia Arrogante-Funes, Fátima Martín-González, Fidel Novillo, Carlos J. Fernández, Rubén R. Vázquez-Jiménez, René Alarcón-Paredes, Antonio Alonso-Silverio, Gustavo A. Cantu-Ramirez, Claudia A. Ramos-Bernal, Rocío N. |
author_facet | Bruzón, Adrián G. Arrogante-Funes, Patricia Arrogante-Funes, Fátima Martín-González, Fidel Novillo, Carlos J. Fernández, Rubén R. Vázquez-Jiménez, René Alarcón-Paredes, Antonio Alonso-Silverio, Gustavo A. Cantu-Ramirez, Claudia A. Ramos-Bernal, Rocío N. |
author_sort | Bruzón, Adrián G. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8535781 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85357812021-10-23 Landslide Susceptibility Assessment Using an AutoML Framework Bruzón, Adrián G. Arrogante-Funes, Patricia Arrogante-Funes, Fátima Martín-González, Fidel Novillo, Carlos J. Fernández, Rubén R. Vázquez-Jiménez, René Alarcón-Paredes, Antonio Alonso-Silverio, Gustavo A. Cantu-Ramirez, Claudia A. Ramos-Bernal, Rocío N. Int J Environ Res Public Health Article 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. MDPI 2021-10-19 /pmc/articles/PMC8535781/ /pubmed/34682717 http://dx.doi.org/10.3390/ijerph182010971 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bruzón, Adrián G. Arrogante-Funes, Patricia Arrogante-Funes, Fátima Martín-González, Fidel Novillo, Carlos J. Fernández, Rubén R. Vázquez-Jiménez, René Alarcón-Paredes, Antonio Alonso-Silverio, Gustavo A. Cantu-Ramirez, Claudia A. Ramos-Bernal, Rocío N. Landslide Susceptibility Assessment Using an AutoML Framework |
title | Landslide Susceptibility Assessment Using an AutoML Framework |
title_full | Landslide Susceptibility Assessment Using an AutoML Framework |
title_fullStr | Landslide Susceptibility Assessment Using an AutoML Framework |
title_full_unstemmed | Landslide Susceptibility Assessment Using an AutoML Framework |
title_short | Landslide Susceptibility Assessment Using an AutoML Framework |
title_sort | landslide susceptibility assessment using an automl framework |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8535781/ https://www.ncbi.nlm.nih.gov/pubmed/34682717 http://dx.doi.org/10.3390/ijerph182010971 |
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