Landslide susceptibility mapping using an entropy index-based negative sample selection strategy: A case study of Luolong county

Abstract

Landslides constitute a significant geological hazard in China, particularly in high-altitude regions like the Himalayas, where the challenging environmental conditions impede field surveys. This research utilizes the IOE model to refine non-landslide samples and integrates it with multiple machine learning models to conduct a comprehensive assessment of landslide susceptibility in Luolong County, Tibet. The IOE model objectively assigns weights to conditioning factors based on the degree of data dispersion, thereby enhancing the predictive accuracy when combined with machine learning models. This research employed Google Earth satellite imagery to construct a comprehensive database comprising 2517 landslide debris in Luolong County. Twelve conditioning factors were identified, encompassing geological environment, topography, meteorology, hydrology, vegetation, soil, and human activities. The IOE model was integrated with SVC, MLP, LDA, and LR models to systematically evaluate landslide susceptibility in Luolong County. The results demonstrate that, after optimizing the non-landslide samples, the coupled models significantly outperformed the unoptimized models in terms of AUC, accuracy, precision, and F1 score. The ranking of classification performance and effect among the four coupled models is IOE-MLP > IOE-SVC > IOE-LR > IOE-LDA. Notably, the AUC value of the IOE-MLP coupled model increased from 0.8172 to 0.9747. Moreover, in the extremely high susceptibility zones, the IOE-MLP model had the highest landslide frequency ratio among the four coupled models, demonstrating the optimal classification performance and the best classification effect. The study identifies land use, elevation, and slope as the predominant controlling factors conditioning landslides in Luolong County. The regions with the highest susceptibility to landslides in Luolong County are predominantly situated in the central areas near rivers and roads, whereas the areas with the lowest susceptibility are largely located in the southwestern, northern, and certain central regions at elevations above 4500 m, which are consistently shrouded in snow and ice. This comprehensive method effectively resolves the challenge of selecting non-landslide samples, thereby improving the predictive accuracy of the landslide susceptibility model. The results of this study offer significant insights for disaster prevention, mitigation, and land use planning in analogous geological settings.