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Multi-Hazard Risk Assessment and Landslide Susceptibility Mapping: A Case Study from Bensekrane in Algeria

Author

Listed:
  • Faïla Benzenine

    (Laboratoire EOLE, University of Tlemcen, P.O. Box 230, Tlemcen 13000, Algeria)

  • Mohamed Amine Allal

    (Laboratoire EOLE, University of Tlemcen, P.O. Box 230, Tlemcen 13000, Algeria)

  • Chérifa Abdelbaki

    (Laboratoire EOLE, University of Tlemcen, P.O. Box 230, Tlemcen 13000, Algeria
    Institute for Water and Energy Sciences Including Climate Change, Pan-African University, Pôle Chetouane, P.O. Box 119, Tlemcen 13000, Algeria)

  • Navneet Kumar

    (Department of Ecology and Natural Resources Management, Center for Development Research (ZEF), University of Bonn, Genscherallee 3, 53113 Bonn, Germany)

  • Mattheus Goosen

    (Office of Research and Graduate Studies, Alfaisal University, P.O. Box 50927, Riyadh 11533, Saudi Arabia)

  • John Mwangi Gathenya

    (Department of Soil, Water and Environmental Engineering, Jomo Kenyatta University of Agriculture and Technology, Nairobi P.O. Box 62000-00200, Kenya)

Abstract

Landslides and their disastrous consequences on the environment and human life have emphasized the need for a better understanding of the dangers associated with slope movement. The objective of this research was to assess and utilize mapping methods for predicting the hazards of landslides and thus to limit the damage of these phenomena more effectively. In the current investigation, multi-hazard mapping was employed in evaluating the risk of slope movements for the municipality of Bensekrane in Tlemcen in Algeria. There has been no hazard assessment made for the study area although it has factors responsible for triggering landslides. The standard Farès method (arithmetic and probabilistic) was employed, and the results were compared with those obtained from the modified Farès technique (arithmetic and probabilistic), which was developed based on a synthesis or combination of previous approaches. In the modified Farès technique, dynamic factors were also included, such as seismic activity, vegetation cover and groundwater level, and, thus, it was considered more reliable. However, the choice of method depended mainly on the availability of data from the study area. The maps obtained showed that the study area is susceptible to slope movements and will be employed for land use planning. The maps obtained by the arithmetic modified Farès method were different from those obtained by the arithmetic Farès method. The former presented a large part of the surface (88%) with an average hazard, unlike the latter, which presented the largest surface (66%) and a low hazard. The maps generated by the probabilistic modified Farès method showed a surface with a high hazard, unlike that obtained by the probabilistic Farès method, where a high hazard did not exist. These differences between the maps were due to the addition of dynamic factors. It is better to choose the modified Farès method, which takes into account all the factors that exist in reality. In this study, enhanced spatial, natural hazard maps were created using the modified Farès method to better aid decision makers and builders in making correct choices for increased safety and town planning. It is crucial to be able to utilize reliable maps based on multi-hazard risk assessment for land development purposes to lessen the possibility of destructive landslides. The modified Farès method can be applied to any other comparable areas around the world.

Suggested Citation

  • Faïla Benzenine & Mohamed Amine Allal & Chérifa Abdelbaki & Navneet Kumar & Mattheus Goosen & John Mwangi Gathenya, 2023. "Multi-Hazard Risk Assessment and Landslide Susceptibility Mapping: A Case Study from Bensekrane in Algeria," Sustainability, MDPI, vol. 15(3), pages 1-16, February.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2812-:d:1057177
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    References listed on IDEAS

    as
    1. Hamid Reza Pourghasemi & Nitheshnirmal Sadhasivam & Mahdis Amiri & Saeedeh Eskandari & M. Santosh, 2021. "Landslide susceptibility assessment and mapping using state-of-the art machine learning techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(1), pages 1291-1316, August.
    Full references (including those not matched with items on IDEAS)

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