Landslide spatial prediction using data-driven based statistical and hybrid computational intelligence algorithms

Optimization of landslide susceptibility model driven by geological environment: a key challenge for disaster reduction in mountainous areas. Xiaojin County in China has complex geology and active hazards, posing a threat to human and economic security. This study evaluated landslide susceptibility in Xiaojin County with high terrain heterogeneity by comparing three data-driven models - WoE, optimized RF, and RBFNetworks. The spatial correlation between 12 conditioning factors and landslides was analyzed, and the multicollinearity between factors was determined, and the significance of the factors was quantified by MDA and MDG, especially for elevation, soil, and distance to roads. The WoE model exhibits exceptional performance (AUC: 0.899 for training, 0.892 for validation), outperforming RF (AUC: 0.880 for training, 0.874 for validation) and RBFNetwork (AUC: 0.866 for training, 0.863 for validation). The results have significant implications for land development and management in Xiaojin County, while also challenging the machine learning paradigm.