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Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment

Author

Listed:
  • 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
    Research Group on Technologies for Landscape Analysis and Diagnosis (TADAT), Department of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, C/Tulipán s/n, 28933 Móstoles, Madrid, Spain)

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.

Suggested Citation

  • Patricia Arrogante-Funes & Adrián G. Bruzón & Fátima Arrogante-Funes & Rocío N. Ramos-Bernal & René Vázquez-Jiménez, 2021. "Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment," IJERPH, MDPI, vol. 18(22), pages 1-21, November.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:22:p:11987-:d:679518
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    References listed on IDEAS

    as
    1. Viet-Ha Nhu & Ayub Mohammadi & Himan Shahabi & Baharin Bin Ahmad & Nadhir Al-Ansari & Ataollah Shirzadi & John J. Clague & Abolfazl Jaafari & Wei Chen & Hoang Nguyen, 2020. "Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment," IJERPH, MDPI, vol. 17(14), pages 1-23, July.
    2. GongHao Duan & JunChi Zhang & Shuiping Zhang, 2020. "Assessment of Landslide Susceptibility Based on Multiresolution Image Segmentation and Geological Factor Ratings," IJERPH, MDPI, vol. 17(21), pages 1-10, October.
    3. Mohd Idris Nor Diana & Nurfashareena Muhamad & Mohd Raihan Taha & Ashraf Osman & Md. Mahmudul Alam, 2021. "Social Vulnerability Assessment for Landslide Hazards in Malaysia: A Systematic Review Study," Land, MDPI, vol. 10(3), pages 1-19, March.
    4. Adrián G. Bruzón & Patricia Arrogante-Funes & Fátima Arrogante-Funes & Fidel Martín-González & Carlos J. Novillo & Rubén R. Fernández & René Vázquez-Jiménez & Antonio Alarcón-Paredes & Gustavo A. Alon, 2021. "Landslide Susceptibility Assessment Using an AutoML Framework," IJERPH, MDPI, vol. 18(20), pages 1-20, October.
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    Cited by:

    1. Leli Zong & Ming Zhang & Zi Chen & Xiaonan Niu & Guoguang Chen & Jie Zhang & Mo Zhou & Hongying Liu, 2023. "Ecological Risk Assessment of Geological Disasters Based on Probability-Loss Framework: A Case Study of Fujian, China," IJERPH, MDPI, vol. 20(5), pages 1-19, March.

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