Optimization of various machine learning concepts to evaluate landslide susceptibility: XGBoost, k-NN and MLP using PSO algorithm

Landslides significantly threaten natural and built environments, necessitating accurate prediction models for effective hazard mitigation. There is an urgent need to further improve the performance of machine learning algorithms in predicting landslide susceptibility by monitoring the impact of optimization algorithms on the performance of these models. This study evaluates the performance of various machine learning classifiers, including k-Nearest Neighbors (kNN), Multi-Layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), for landslide susceptibility mapping. Additionally, Particle Swarm Optimization (PSO) is employed to enhance model performance by optimizing hyperparameters. Mountainous areas in the eastern Mediterranean (the northern Kabir River basin in western Syria) were identified as a result of the high frequency of landslide events over the past two decades. Nineteen factors causing landslides were identified, with no factor excluded, as a result of a multicollinearity test. The results indicate that XGBoost achieves the highest performance among traditional models. When integrated with PSO, the PSO-XGBoost model further improves classification performance, demonstrating its robustness in handling complex spatial patterns. Feature importance analysis using SHAP confirms slope as the dominant factor, followed by TRI, rainfall, Aspect, TWI, and curvature, highlighting the role of topography and hydrology in landslide occurrence. Moderate lithology, NDVI, and LULC contributions and lower importance of Flow Accumulation and Soil Depth suggest complex environmental interactions. Model predictions show varying susceptibility distributions. PSO-MLP assigns the highest very high susceptibility (44.09%), while PSO-XGBoost provides a balanced classification (31.13%). The PSO-XGBoost model demonstrates superior predictive capability, offering reliable landslide susceptibility maps for disaster risk management and land-use planning. © The Author(s) 2026.