Mapping landslide susceptibility in the Eastern Mediterranean mountainous region: a machine learning perspective

Assessing landslide susceptibility is essential in urban planning and risk management. In the context of the Eastern Mediterranean region, there is a continuing need to compare the performance of machine learning (ML) algorithms in predicting landslide susceptibility, which can improve landslide risk management measures. Therefore, this study aims to evaluate and compare the predictive capabilities of three ML models: Multilayer perceptron (MLP), Light Gradient Boosting Machine (LGBM), and Extreme Gradient Boosting (XGBoost) models, in evaluating the susceptibility of various types of landslides and to refine the combination of causal factors. An evaluation of 19 conditioning factors, including topographical, geological, and environmental variables, was conducted to assess their effects on landslide susceptibility in different models in a geographic information system (GIS) environment. The results show that "Elevation" and "Slope" were consistently identified as the most influential factors in all models, with MLP demonstrating the greatest sensitivity to "Elevation." The study area was divided into five susceptibility categories: very low, low, moderate, high, and very high. According to the LGBM model, 24.27% of the area was classified as "very low" susceptibility, while the XGBoost and MLP models identified 25.69% and 27.28%, respectively. On the other hand, the "very high" susceptibility category covered 19.57%, 20.31%, and 19.78% of the area for the LGBM, XGBoost, and MLP models, respectively. The AUC-ROC approach has been utilized to evaluate, validate, and compare the performance of different ML models. Our study found AUC values for three MLTs. These findings suggest that all models demonstrate reasonable accuracy in identifying susceptible zones, and XGBoost demonstrated the best performance among the MLTs, with an AUC of 92.6% compared to the others. The insights gained from this study can inform targeted mitigation strategies to reduce landslide risks in Lebanon. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.