Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment

Patricia Arrogante-Funes, Adrián G. Bruzón, Fátima Arrogante-Funes, Rocío N. Ramos-Bernal and René Vázquez-Jiménez
Additional contact information
Patricia Arrogante-Funes: Department of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, C/Tulipán s/n, 28933 Móstoles, Madrid, Spain
Adrián G. Bruzón: Department of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, C/Tulipán s/n, 28933 Móstoles, Madrid, Spain
Fátima Arrogante-Funes: Departamento de Geografía, Geología y Medio Ambiente, Facultad de Filosofía y Letras, Universidad de Alcalá, Área de Geografía, GITA, C/Colegios 2, 28801 Alcalá de Henares, Madrid, Spain
Rocío N. Ramos-Bernal: Cuerpo Académico UAGro CA-93 Riesgos Naturales y Geotecnología, FI, Universidad Autónoma de Guerrero, Av/Lázaro Cárdenas s/n, CU, Chilpancingo 39070, Mexico
René Vázquez-Jiménez: Cuerpo Académico UAGro CA-93 Riesgos Naturales y Geotecnología, FI, Universidad Autónoma de Guerrero, Av/Lázaro Cárdenas s/n, CU, Chilpancingo 39070, Mexico

IJERPH, 2021, vol. 18, issue 22, 1-21

Abstract: 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.

Keywords: landslide; hazard assessment; landslide risk; vulnerability; susceptibility; ecological values (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/22/11987/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/22/11987/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:22:p:11987-:d:679518

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().