Landslides are considered as one among many phenomena jeopardizing human beings as well as their constructions.To prevent this disastrous problem,researchers have used several approaches for landslide susceptibility modeling,for the purpose of preparing accurate maps marking landslide prone areas.Among the most frequently used approaches for landslide susceptibility mapping is the Artificial Neural Network(ANN)method.However,the effectiveness of ANN methods could be enhanced by using hybrid metaheuristic algorithms,which are scarcely applied in landslide mapping.In the current study,nine hybrid metaheuristic algorithms,genetic algorithm(GA)-ANN,evolutionary strategy(ES)-ANN,ant col-ony optimization(ACO)-ANN,particle swarm optimization(PSO)-ANN,biogeography based optimization(BBO)-ANN,gravitational search algorithm(GHA)-ANN,particle swarm optimization and gravitational search algorithm(PSOGSA)-ANN,grey wolves optimization(GWO)-ANN,and probability based incre-mental learning(PBIL)-ANN have been used to spatially predict landslide susceptibility in Algiers'Sahel,Algeria.The modeling phase was done using a database of 78 landslides collected utilizing Google Earth images,field surveys,and six conditioning factors(lithology,elevation,slope,land cover,distance to stream,and distance to road).Initially,a gamma test was used to decrease the input variable numbers.Furthermore,the optimal inputs have been modeled by the mean of hybrid metaheuristic ANN tech-niques and their performance was assessed through seven statistical indicators.The comparative study proves the effectiveness of the co-evolutionary PSOGSA-ANN model,which yielded higher performance in predicting landslide susceptibility compared to the other models.Sensitivity analysis using the step-by-step technique was done afterward,which revealed that the distance to the stream is the most influential factor on landslide susceptibility,followed by the slope factor which ranked second.Lithology and the distance to road have demonstrated a moderate effect on landslide susceptibility.Based on these findings,an accurate map has been designed to help land-use managers and decision-makers to mitigate landslide hazards.

Technology Department,école Normale Supérieure D'Enseignement Technologique de Skikda(ENSET),Skikda,Algeria;Laboratoire Central des Travaux Publics(LCTP),Algiers,Algeria

[1]Mohammed Amin Benbouras-.Hybrid meta-heuristic machine learning methods applied to landslide susceptibility mapping in the Sahel-Algiers)[J].国际泥沙研究（英文版）,2022(05):601-618

Algiers,jeopardizing,ony,GHA,PSOGSA,PBIL

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