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Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment
Among the numerous natural hazards, landslides are one of the greatest, as they can cause enormous loss of life and property, and affect the natural ecosystem and their services. Landslides are disasters that cause damage to anthropic activities and innumerable loss of human life, globally. The land...
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/PMC8623781/ https://www.ncbi.nlm.nih.gov/pubmed/34831741 http://dx.doi.org/10.3390/ijerph182211987 |
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author | Arrogante-Funes, Patricia Bruzón, Adrián G. Arrogante-Funes, Fátima Ramos-Bernal, Rocío N. Vázquez-Jiménez, René |
author_facet | Arrogante-Funes, Patricia Bruzón, Adrián G. Arrogante-Funes, Fátima Ramos-Bernal, Rocío N. Vázquez-Jiménez, René |
author_sort | Arrogante-Funes, Patricia |
collection | PubMed |
description | Among the numerous natural hazards, landslides are one of the greatest, as they can cause enormous loss of life and property, and affect the natural ecosystem and their services. Landslides are disasters that cause damage to anthropic activities and innumerable loss of human life, globally. The landslide risk assessed by the integration of susceptibility and vulnerability maps has recently become a manner of studying sites prone to landslide events and managing these regions well. Developing countries, where the impact of landslides is frequent, need risk assessment tools that enable them to address these disasters, starting with their prevention, with free spatial data and appropriate models. Our study shows a heuristic risk model by integrating a susceptibility map made by AutoML and a vulnerability one that is made considering ecological vulnerability and socio-economic vulnerability. The input data used in the State of Guerrero (México) approach uses spatial data, such as remote sensing, or official Mexican databases. This aspect makes this work adaptable to other parts of the world because the cost is low, and the frequency adaptation is high. Our results show a great difference between the distribution of vulnerability and susceptibility zones in the study area, and even between the socio-economic and ecological vulnerabilities. For instance, the highest ecological vulnerability is in the mountainous zone in Guerrero, and the highest socio-economic vulnerability values are found around settlements and roads. Therefore, the final risk assessment map is an integrated index that considers susceptibility and vulnerability and would be a good first attempt to challenge landslide disasters. |
format | Online Article Text |
id | pubmed-8623781 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86237812021-11-27 Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment Arrogante-Funes, Patricia Bruzón, Adrián G. Arrogante-Funes, Fátima Ramos-Bernal, Rocío N. Vázquez-Jiménez, René Int J Environ Res Public Health Article Among the numerous natural hazards, landslides are one of the greatest, as they can cause enormous loss of life and property, and affect the natural ecosystem and their services. Landslides are disasters that cause damage to anthropic activities and innumerable loss of human life, globally. The landslide risk assessed by the integration of susceptibility and vulnerability maps has recently become a manner of studying sites prone to landslide events and managing these regions well. Developing countries, where the impact of landslides is frequent, need risk assessment tools that enable them to address these disasters, starting with their prevention, with free spatial data and appropriate models. Our study shows a heuristic risk model by integrating a susceptibility map made by AutoML and a vulnerability one that is made considering ecological vulnerability and socio-economic vulnerability. The input data used in the State of Guerrero (México) approach uses spatial data, such as remote sensing, or official Mexican databases. This aspect makes this work adaptable to other parts of the world because the cost is low, and the frequency adaptation is high. Our results show a great difference between the distribution of vulnerability and susceptibility zones in the study area, and even between the socio-economic and ecological vulnerabilities. For instance, the highest ecological vulnerability is in the mountainous zone in Guerrero, and the highest socio-economic vulnerability values are found around settlements and roads. Therefore, the final risk assessment map is an integrated index that considers susceptibility and vulnerability and would be a good first attempt to challenge landslide disasters. MDPI 2021-11-15 /pmc/articles/PMC8623781/ /pubmed/34831741 http://dx.doi.org/10.3390/ijerph182211987 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 Arrogante-Funes, Patricia Bruzón, Adrián G. Arrogante-Funes, Fátima Ramos-Bernal, Rocío N. Vázquez-Jiménez, René Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment |
title | Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment |
title_full | Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment |
title_fullStr | Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment |
title_full_unstemmed | Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment |
title_short | Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment |
title_sort | integration of vulnerability and hazard factors for landslide risk assessment |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8623781/ https://www.ncbi.nlm.nih.gov/pubmed/34831741 http://dx.doi.org/10.3390/ijerph182211987 |
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