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

Fatiha Debiche, Mohammed Amin Benbouras, Alexandru-Ionut Petrisor (), Lyes Mohamed Baba Ali and Abdelghani Leghouchi
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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
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

Land, 2024, vol. 13, issue 6, 1-29

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.

Keywords: Adaptive Neuro-Fuzzy Inference System; hybrid metaheuristic optimization algorithms; landslide susceptibility; geographical information system; K cross-validation approach (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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