Integration of multi-criteria decision analysis and statistical models for landslide susceptibility mapping in the western Algiers Province (Algeria) using GIS techniques and remote sensing data

Landslide susceptibility assessment and prediction are among the main processing for disaster management and land use planning activities. Therefore, the general purpose of this research was to evaluate GIS-based spatial modeling of landslides in the western Algiers Province using five models, namely, frequency ratio (FR), weights of evidence (WoE), evidential belief function (EBF), logistic regression (LR), and analytical hierarchy process (AHP), and then compare their performances. At first, a landslide inventory map was prepared according to Google Earth satellite images, historical records, and extensive field surveys. The recorded landslides were divided into two groups (70% and 30%) to establish the training and validation models. In the next step, GIS techniques and remote sensing data were used, to prepare a spatial database containing 13 landslide conditioning factors: lithology, distance to lithological boundaries, permeability, slope, exposure, altitude, profile curvature, plan curvature, precipitation, distance to rivers, topographic wetness index, normalized difference vegetation index, and distance to roads. Finally, the landslide susceptibility maps were produced using the five models and validated by the areas under the relative operative characteristic curve (AUC). The AUC results showed a significant improvement in susceptibility map accuracy; the FR model has the best performance in the training and prediction process (90%), followed by LR (88%, 89%), WoE (88%, 87%), EBF (86%,86%), and AHP (76%,75%), respectively. The produced maps in the current study could be useful for land use planning and hazard mitigation purposes in western Algiers Province.