Landslide Hazard Assessment in the Heterogeneous Geomorphological and Environmental Context of the Rif Region, Morocco – A Machine Learning Approach
Geosciences, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University, Avenue Ibn Batouta, Rabat 10100, Morocco
Electrical and Computer Engineering Department, Seattle University, Seattle, WA 98122, USA
Géoscience Environnement Toulouse, IRD, CNRS, UPS, OMP, Mixed Research Unit 5563, 14 Av. E. Belin, 31400 Toulouse, France
Mixed Research Unit EMMAH (Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes), Hydrogeology Laboratory, Avignon University, 84916 Avignon, France
NorthWest Research Associates & Pan Ocean Remote Sensing Association, Seattle, WA 98105, USA
Autor do korespondencji
Maryem Hamidi   

Geosciences, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University, Avenue Ibn Batouta, Rabat 10100, Morocco
Ecol. Eng. Environ. Technol. 2023; 8:272–292
Landslides are considered to be one of the most significant and critical natural hazards in the heterogeneous geomorphological setting of the Rif region of Morocco. Despite the high susceptibility to landslides, the region lacks detailed studies. Therefore, this research introduces four advanced machine learning methods, namely Support Vector Machine (SVM), Classification and Regression Trees (CART), Multivariate Discriminant Analysis (MDA), and Logistic Regression (LR), to perform landslide susceptibility mapping, as well as study of the connection between landslide occurrence and the complex regional geo-environmental context of Taounate province. Fifteen causative factors were extracted, and 255 landslide events were identified through fieldwork and satellite imagery analysis. All models performed very well (AUC > 0.954), while the CART model performed the best (AUC= 0.971). However, SVM demonstrated superior performance compared to other methods, achieving the highest accuracy (89.92%) and F1-measure (81.66%) scores on the training data, and the highest accuracy (83.01%), precision (81.74%), and specificity (79.46%) scores on the test data. The results do not necessarily indicate that LR and MDA have the lowest predictive ability, as they demonstrated high accuracy in terms of AUC and in some classification tasks. Moreover, they provide the significant advantage of easy interpretation of the geo-environmental processes that control landslides. Rainfall is the primary triggering factor of landslides in the study area. The majority of landslides occurred on slopes, particularly those located along rivers and faults, suggesting that landslides in the region are closely associated with active tectonics and precipitation. All four models predicted similar spatial distribution patterns in landslide susceptibility. The results showed that almost half of the area mainly in the north and northwest, has a very high susceptibility to landslides. The findings provide valuable references for land use management and the implementation of effective measures for landslide prevention.