LANDSLIDE SUSCEPTIBILITY MAPPING USING MACHINE LEARNING ALGORITHMS STUDY CASE AL HOCEIMA REGION, NORTHERN MOROCCO

Abstract. Landslides are one of the most dangerous natural disasters worldwide. Al Hoceima region, Part of the Moroccan mountain chain of the Rif is not an exception, since it’s dominated by relatively young reliefs and marked by its dynamics compared to other regions. The main goal of this study is to assess the performance of Machine learning algorithms and identify the optimal method for the mapping of the area susceptible to landslides, in Al Hoceima city and its periphery, The current study aimed at evaluating the capabilities of six advanced machine learning algorithms including, Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), XGBoost (XGB) and Logistic Regression (LR). A total of 114 landslides were mapped from various sources. 70% of this database was used for model building and 30% for validation. Ten landslide factors are selected to detect the most sensitive areas: altitude, slope, aspect, distance to faults, distance from roads, lithology, curvature, plan curvature, profile curvature, and vegetation index (NDVI). The outcome of the landslide susceptibility analysis was verified using receiver operating characteristics (ROC) curves and precision-recall curves (PRC), acknowledging XGBoost and Random Forest as the optimal methods with AUCROC (96% and 95.5% accordingly), a severe imbalanced classification was detected by the PRC, solved by undersampling the majority class, to obtain major improvement in models performance according to AUCPRC (from 40% to 87% for XGBoost) and slight decrease according to AUCROC (from 96% to 94% for XGBoost). The outcome of this study and the landslide susceptibility maps would be useful for environmental, economic, and social protection and to help formulate suggestions for optimizing landslide risk assessment in areas exposed to this phenomenon.