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Advancing Landslide Susceptibility Mapping in the Medea Region Using a Hybrid Metaheuristic ANFIS Approach

Author

Listed:
  • Fatiha Debiche

    (Structure and Materials Department, University of Science and Technology Houari Boumediene, Algiers 16024, Algeria)

  • Mohammed Amin Benbouras

    (Structure and Materials Department, University of Science and Technology Houari Boumediene, Algiers 16024, Algeria)

  • Alexandru-Ionut Petrisor

    (Doctoral School of Urban Planning, Ion Mincu University of Architecture and Urbanism, 10014 Bucharest, Romania
    Department of Architecture, Faculty of Architecture and Urban Planning, Technical University of Moldova, 2004 Chisinau, Moldova
    National Institute for Research and Development in Constructions, Urbanism and Sustainable Spatial Development URBAN-INCERC, 21652 Bucharest, Romania
    National Institute for Research and Development in Tourism, 50741 Bucharest, Romania)

  • Lyes Mohamed Baba Ali

    (Faculty of Earth Sciences, Geography and Territorial Planning, University of Science and Technology Houari Boumediene, Algiers 16024, Algeria)

  • Abdelghani Leghouchi

    (Civil Engineering Department, Mohammed Seddik Benyahia University, Jijel 18000, Algeria)

Abstract

Landslides pose significant risks to human lives and infrastructure. The Medea region in Algeria is particularly susceptible to these destructive events, which result in substantial economic losses. Despite this vulnerability, a comprehensive landslide map for this region is lacking. This study aims to develop a novel hybrid metaheuristic model for the spatial prediction of landslide susceptibility in Medea, combining the Adaptive Neuro-Fuzzy Inference System (ANFIS) with four novel optimization algorithms (Genetic Algorithm—GA, Particle Swarm Optimization—PSO, Harris Hawks Optimization—HHO, and Salp Swarm Algorithm—SSA). The modeling phase was initiated by using a database comprising 160 landslide occurrences derived from Google Earth imagery; field surveys; and eight conditioning factors (lithology, slope, elevation, distance to stream, land cover, precipitation, slope aspect, and distance to road). Afterward, the Gamma Test (GT) method was used to optimize the selection of input variables. Subsequently, the optimal inputs were modeled using hybrid metaheuristic ANFIS techniques and their performance evaluated using four relevant statistical indicators. The comparative assessment demonstrated the superior predictive capabilities of the ANFIS-HHO model compared to the other models. These results facilitated the creation of an accurate susceptibility map, aiding land use managers and decision-makers in effectively mitigating landslide hazards in the study region and other similar ones across the world.

Suggested Citation

  • Fatiha Debiche & Mohammed Amin Benbouras & Alexandru-Ionut Petrisor & Lyes Mohamed Baba Ali & Abdelghani Leghouchi, 2024. "Advancing Landslide Susceptibility Mapping in the Medea Region Using a Hybrid Metaheuristic ANFIS Approach," Land, MDPI, vol. 13(6), pages 1-29, June.
  • Handle: RePEc:gam:jlands:v:13:y:2024:i:6:p:889-:d:1418009
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    References listed on IDEAS

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    1. Binh Thai Pham & Ataollah Shirzadi & Himan Shahabi & Ebrahim Omidvar & Sushant K. Singh & Mehebub Sahana & Dawood Talebpour Asl & Baharin Bin Ahmad & Nguyen Kim Quoc & Saro Lee, 2019. "Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms," Sustainability, MDPI, vol. 11(16), pages 1-25, August.
    2. Donatella Caniani & Stefania Pascale & Francesco Sdao & Aurelia Sole, 2008. "Neural networks and landslide susceptibility: a case study of the urban area of Potenza," 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. 45(1), pages 55-72, April.
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