## 6 Conclusions  
This study selected Lishui, southern China, as the study area, where landslides frequently occur due to factors such as human activities, terrain, and geological structures. Refining the susceptibility mapping research in this area is of great practical significance for disaster reduction and prevention. 1. This study employed multi-temporal optical remote sensing images and the IV method to refine the selection of landslide and non-landslide points within traditional machine learning models. This process effectively alleviated the dimensional influence among different features. Due to the removal of dimensionality differences, the data that are fed into the model for IV-based classification result from calculations. Compared to ensemble learning models such as LightGBM and XGBoost, CatBoost autonomously manages categorical features, reducing the complexity of feature preprocessing. Additionally, it is less susceptible to outliers and noise, ensuring a more stable and accurate model. 2. Using the hyperopt framework for hyperparameter optimization, we constructed the IV-CatBoost model, achieving an RMSE of 0.24, an R\({}^{2}\) of 0.77, and an MAE of 0.16, demonstrating a high level of accuracy. In the resulting model, 90.71% of landslide points are located within the VH and H regions, with only 1.06% falling within the VL region, resulting in an AUC value of 0.88. Figure 22: Distribution of predicted values across (**a**) elevation, (**b**) slope, (**c**) roughness, and (**d**) land use. 3. To make the model interpretable, SHAP values were computed for the features, revealing the ranking of feature importance and their distributions. The results showed that the distance to roads and terrain factors have relatively significant impacts on the model, aligning with optical remote sensing interpretation results and on-site conditions, thus reflecting the model's accuracy. 4. This study requires obtaining long-term surface deformation information in the research area to precisely understand the slope conditions. Therefore, DInSAR has limitations, and it is recommended to choose PS-InSAR and SBAS-InSAR, which can capture extended temporal sequences. Given that the research area is hilly, with most regions being mountainous, to ensure accurate computation results, we selected the SBAS-InSAR method for this InSAR calculation to acquire precise surface deformation information in the research area. The SBAS-InSAR results indicate that the majority of the regions exhibit annual deformation between \(-10.0\) mm/y and \(10.0\) mm/y. 5. By integrating SBAS-InSAR technology, the existing IV-CatBoost model was augmented with dynamic deformation factors to establish the IVCI model. As a result, 165,784 grids attained enhanced susceptibility classification precision through InSAR-driven refinements, yielding an AUC value of 0.90. Quantitatively, the IVCI model's accuracy surpasses that of the IV-CatBoost model. 6. By analyzing the differences between the IV-CatBoost model and the IVCI model, three areas with significant modifications were identified. Validation using optical remote sensing images and SBAS-InSAR results confirmed that these areas are in an unstable state, and the results of the IV-CatBoost model did not align with the ground truth, leading to misclassifications. This illustrates that the introduction of InSAR technology can effectively identify and correct classification errors in the model, further enhancing the accuracy of the susceptibility model. In conclusion, the IVCI model established based on the judgment matrix can refine traditional susceptibility mapping, making susceptibility zoning more accurate and providing valuable references and data support for local disaster reduction, prevention, and engineering projects.