Refined Landslide Susceptibility Mapping by Integrating the SHAP-CatBoost Model and InSAR Observations: A Case Study of Lishui, Southern China  
[PERSON]  
1 College of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China; [EMAIL_ADDRESS] (Z.Y. ); [EMAIL_ADDRESS] (Y.S. ); [EMAIL_ADDRESS] (Z.Y.) [PERSON]  
2 Key Laboratory of Safety Engineering and Technology Research of Zhejiang Province, Hangzhou 310012, China; [EMAIL_ADDRESS]  
[PERSON]  
1 College of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China; [EMAIL_ADDRESS] (Y.S. ); [EMAIL_ADDRESS] (Z.Y.) [PERSON]  
3 Key Laboratory of Western China's Mineral Resources and Geological Engineering, Ministry of Education, Chang'an University, Xi'an 710054, China; [EMAIL_ADDRESS] (Y.S.) [PERSON]  
1 College of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China; [EMAIL_ADDRESS] (Z.Y.) [PERSON]  
4 College of Construction Engineering, Jilin University, Changchun 130026, China; [EMAIL_ADDRESS] (Z.Y.) [PERSON]  
1 College of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China; [EMAIL_ADDRESS] (Z.Y.) [PERSON]  
1 College of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China; yuab@jlu. landslide susceptibility holds significant practical importance for disaster mitigation and prevention [3; 4]. Landslide susceptibility provides a visual representation of the spatial probability of landslide disasters in different regions. It serves as a critical tool for landslide prevention, monitoring, mitigation, and assessment in geoscience practices. Landslide susceptibility models can be broadly classified into two categories: qualitative and quantitative [5; 6]. Common qualitative models include analytic hierarchy processes (AHP) [7; 8] and fuzzy logic models [9]. Common quantitative models can be broadly categorized into statistical models, machine learning models, and deep learning models. Statistical models mainly include frequency ratio models [10; 11], logistic regression (LR) models [11; 12] and information value (IV) models [13]; machine learning models primarily consist of random forests (RF) [14; 15] and support vector machines (SVM) [16; 17]; deep learning models mainly encompass convolutional neural networks (CNN) [18; 19] and deep neural networks (DNN) [20; 21]. Traditional qualitative models are heavily influenced by subjective human factors, whereas machine learning models demonstrate better performance in handling high-dimensional nonlinear data, such as susceptibility assessment. They have yielded accurate results in susceptibility evaluations in many regions [22]. In recent years, many scholars have incorporated deep learning models into landslide susceptibility research [18; 23]. These models exhibit higher accuracy compared to traditional machine learning models. However, their performance in susceptibility assessment is constrained by variations in geographical regions and landslide sample sizes. Meanwhile, ensemble machine learning models have emerged prominently in machine learning, as they mitigate overfitting risks while enhancing model accuracy and stability [24]. Ensemble machine learning models, such as Boosting, Bagging, Stacking, AdaBoost, and Gradient Boosting, have been successfully applied in the field of landslide susceptibility. Moreover, several researchers have improved the accuracy and robustness of landslide susceptibility prediction by combining predictions from multiple models. [PERSON] [25] successfully applied eXtreme Gradient Boosting (XGBoost) to landslide susceptibility research. [PERSON] et al. [26] achieved higher accuracy in landslide susceptibility assessment in the Wanzhou District by using the latest boosting ensemble algorithms, such as Light Gradient Boosting (LightGBM) and CatBoost, which efficiently address issues related to categorical features, gradient bias, and prediction shift and thus improved the algorithm's accuracy and generalization capabilities.