Exploration and Comparison of the Effect of Conventional and Advanced Modeling Algorithms on Landslide Susceptibility Prediction: A Case Study from Yadong Country, Tibet  
[PERSON]  
1 Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China;  
[EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (W.P. ); [EMAIL_ADDRESS] (WL. );  
[EMAIL_ADDRESS] (H.H. ); 13632350538163.com (J.H. ); [EMAIL_ADDRESS] (K.L.) Guangzhou Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China  
guangzhou Collaborative Innovation Center of Natural Resources Planning and Marine Technology, Guangzhou 510060, China  
1  
[PERSON]  
1 Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China;  
[EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (W.P. ); [EMAIL_ADDRESS] (WL. );  
[EMAIL_ADDRESS] (H.H. ); 13632350538163.com (J.H. ); [EMAIL_ADDRESS] (K.L.) Guangzhou Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China  
guangzhou 510060, China  
1  
[PERSON]  
1 Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China;  
[EMAIL_ADDRESS] (WL. );  
[EMAIL_ADDRESS] (H.H. ); 13632350538163.com (J.H. ); [EMAIL_ADDRESS] (K.L.) Guangzhou Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China  
guangzhou Collaborative Innovation Center of Natural Resources Planning and Marine Technology, Guangzhou 510060, China  
on predicting potential disaster areas [6; 7]. To minimize the hazard and risk of landslides, landslide susceptibility mapping (LSM) is widely recognized as a proven approach. The precision of LSM is contingent upon dependable data and modeling algorithms. Throughout time, a range of algorithms have been utilized in LSM, allowing for incremental improvements in accuracy. Both quantitative (data-driven) and qualitative (knowledge-based or physically based) algorithms are viable options for landslide susceptibility modeling [8; 9]. Data-driven algorithms are usually classified as bivariate methods (such as certainty factor (CF) and information value (IV)), multivariate methods (such as logistic regression (LR) and cluster analysis), conventional machine learning [10; 11; 12; 13], and deep learning [14]. Notwithstanding the fact that knowledge-based methods tend to be subjective and confined to small-scale domains, physically based methods, which rely on physical experiments, often entail significant time and financial resources and exhibit limited scalability. In recent years, the advent of advanced remote sensing has facilitated the acquisition of landslide samples and critical factors, rendering data-driven approaches more accessible and precise. Statistical methods are sensitive to the distribution and collinearity of data, and certain assumptions must be satisfied before application. There have been a number of conventional machine learning methods (such as random forest and categorical boosting (CatBoost) observed due to their ability to solve non-linear geo-environmental problems without making unnecessary assumptions [15; 16]. The structure of conventional machine learning methods is relatively simple, usually involving only 1-2 hidden layers. While certain studies have undertaken a thorough examination and comparison of conventional statistical and machine learning models, a consensus has yet to be reached regarding the optimal or most appropriate model [17; 18]. Deep learning represents a progression beyond machine learning, exhibiting superior performance in identifying representative features and potentially enhancing classification accuracy [19; 20; 21]. Convolutional neural networks (CNN), a prominent example of deep learning, have been extensively validated in domains such as image classification and facial recognition, yet their application to LSM remains limited [22; 23; 24]. A multi-level structure of CNN is constructed to explore the complex non-linear relationships between variables, which is accorded with the characteristics of LSM.