Application of machine learning in the assessment of landslide susceptibility: A case study of mountainous eastern Mediterranean region, Syria

Abstract

Landslide is a considerable geomorphological risk in terrain systems worldwide. Advanced techniques present a unique tool for predicting landslide susceptibility with unbiased and precise outputs. However, the application of this tool to analyze landslide susceptibility in the eastern Mediterranean landscape is still not sufficiently understood. This study aimed to assess the implementation of three machine learning (ML) algorithms, i.e., support vector machine (SVM), random forest (RF) and extreme gradient boost (XGBoost), in landslide susceptibility mapping in the mountainous area of western Syria. In this regard, 200 landslide events were inventoried from historical data, aerial images and conducted fieldworks. Sixteen triggering factors were selected according to the literature and geographical features (Monsoon period). The receiver operating characteristic (ROC) outcomes revealed that the RF achieved better performance with an area under curve (AUC) of 0.96, pursued by XGBoost and SVM with AUC of 0.94 and 0.90, respectively. This assessment presents an essential understanding of the effective implementation of ML in landslide assessment in the mountainous region of the eastern Mediterranean. We emphasized, hence, that the RF algorithm has the most robust performance of landslide susceptibility prediction in the western Syria. Moreover, the outputs of this study will provide local decision-makers with insights to produce regional management strategies for the landslide, especially after the Syrian war phase.