Landslides Susceptibility Mapping Using Analytic Hierarchy Process: Sahel of Algiers case study

Filali, Mira; Belhadj, Fatma Zohra

doi:10.22059/ceij.2025.377379.2073

Landslides Susceptibility Mapping Using Analytic Hierarchy Process: Sahel of Algiers case study

Articles in Press

Document Type : Research Papers

Authors

Mira Filali ¹
Fatma Zohra Belhadj ²

¹ PRAVDUR Laboratory, Faculty of Natural and Earth Sciences, Djilali Bounaama University of Khemis Miliana, Algeria.

² Built Environmental Research Lab, Faculty of Civil Engineering, University of Science and Technology Houari Boumediene, National Higher School of Public Works (ENSTP), Basic Infrastructure Department (DIB), Algeria.

10.22059/ceij.2025.377379.2073

Abstract

Geological hazards present a major challenge to the development of Algiers, the capital of Algeria, with landslides being particularly prevalent in the early terrain of the Sahel region. Comprehensive preliminary studies are essential for mapping zones vulnerable to slope instability and for mitigating their impacts. This study aims to produce a landslide susceptibility map for the Marly Sahel area using Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) methodologies. Key factors considered in the susceptibility assessment include slope degree, lithology, distance to drainage, elevation, landuse and geotechnical parameters. The weight of each factor class was determined using the AHP technique integrated with the GIS functionalities. This process resulted in the generation of landslide Susceptibility maps, categorizing the area into five zones: very low, low, moderate, high, and very high susceptibility. The analysis identified slope, lithology, cohesion, elevation and proximity to drainage as the most influential factors contributing to landslides occurrence. The study revealed that the northern and south-eastern parts of the area, particullary near valleys and drainage systems, are especialy susceptible to landslides. A landslide inventory map was employed to validate the susceptibility model, achieving a prediction rate of 0.75 based on the area under curve (AUC) technique.

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