Assessment of landslide susceptibility using machine learning classifiers in Ziz upper watershed, SE Morocco

Abstract

ABSTRACT Landslides present a significant hazard to human life, infrastructure, and property, particularly in mountainous regions. In Morocco, these risks have garnered increased attention due to their detrimental impact. This study seeks to model landslide susceptibility using three machine learning classifiers (MLCs): Multi-Layer Perceptron (MLP), Random Forest (RF), and Adaptive Boosting Classifier (AdaBoost), and compare their performance. Initially, 144 landslide sites were identified, and thirteen factors pertaining to landslides were considered. The models’ performance was assessed by calculating the area under the receiver operating characteristic curve (AUC-ROC). The findings reveal that AUC values range from 68.7% for AdaBoost to 82.2% for RF. The generated landslide susceptibility maps can aid decision-makers in avoiding areas with a high susceptibility to landslides.