Landslide susceptibility evaluation and interpretability analysis of typical loess areas based on deep learning

Loess areas have a unique geological environment, and geological disasters occur there frequently. In this work, the typical loess area Lvliang was used as the study area. Using the historical landslide catalog, 12 influencing factors were chosen by integrating multisource heterogeneous spatiotemporal big data such as remote sensing, ground investigation, and basic geography. Based on frequency ratio (FR) and improved TabNet deep learning technology, landslide susceptibility evaluation and uncertainty analysis were performed. The results showed that the TabNet evaluation model using FR and self-supervised learning performs well and has the highest FR in extremely high-prone areas. Compared with other methods, this method has the highest scores in areas under the curve and susceptibility index distribution and the lowest uncertainty. Moreover, the SHAP method was used for interpretability analysis of the model. Therefore, this study can provide new ideas for landslide susceptibility management.