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Advancing landslide susceptibility mapping in the Medea region using a hybrid metaheuristic ANFIS approach

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dc.contributor.author DEBICHE, Fatiha
dc.contributor.author BENBOURAS, Mohammed Amin
dc.contributor.author PETRISOR, Alexandru-Ionut
dc.contributor.author BABA ALI, Lyes Mohamed
dc.contributor.author LEGHOUCHI, Abdelghani
dc.date.accessioned 2025-04-22T17:31:19Z
dc.date.available 2025-04-22T17:31:19Z
dc.date.issued 2024
dc.identifier.citation DEBICHE, Fatiha; Mohammed Amin BENBOURAS; Alexandru-Ionut PETRISOR; Lyes Mohamed BABA ALI and Abdelghani LEGHOUCHI. Advancing landslide susceptibility mapping in the Medea region using a hybrid metaheuristic ANFIS approach. Land. 2024, vol. 13, nr. 6, art. nr. 889. ISSN 2073-445X 2073-445X. en_US
dc.identifier.issn 2073-445X
dc.identifier.uri https://doi.org/10.3390/land13060889
dc.identifier.uri https://repository.utm.md/handle/5014/30968
dc.description Access full text: https://doi.org/10.3390/land13060889 en_US
dc.description.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 en_US
dc.language.iso en en_US
dc.publisher Multidisciplinary Digital Publishing Institute (MDPI) en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject landslide susceptibility en_US
dc.subject geographical information system en_US
dc.subject k cross-validation approach en_US
dc.title Advancing landslide susceptibility mapping in the Medea region using a hybrid metaheuristic ANFIS approach en_US
dc.type Article en_US


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