## 5 Discussion  
With the progress of related technologies and the maturity of theory, various approaches have been developed and applied to LSM to improve prediction accuracy and reliability [45; 46; 47; 48; 49]. Recently, deep learning is becoming popular and beginning to be used for LSM. In this study, CNN was applied to LSM, and its performance was compared to the conventional statistical methods and machine learning approaches. In comparison to the other three models, the CNN model shows superior fitting and generalization capabilities in predicting landslide susceptibility. It has been found that very high landslide susceptibility areas are mainly associated with the Yarlung Zangbo River and its tributaries. A large part of the eroded slopes is scoured by the river network. The areas near streams are densely populated, and the occurrence of landslides can threaten lives. Different algorithms have different emphases and, generally, their performance varies with different study areas [50; 51]. However, advanced algorithms usually perform better in terms of accuracy compared to conventional statistical methods [52; 53]. The result of our study also found that CNN and CatBoost performed better in terms of accuracy and AUC, and CNN did the best in generalization. There was a certain gap between the three models. The improvement benefits from the characteristics of the algorithms themselves for decreasing the bias, discrepancy, and over-fitting problems. It is easy to implement and acceptable in conventional statistical methods to establish a mathematical equation for investigating the relationship between factors related to landslides and landslides occurring. In machine learning methods, optimization is stressed, so the multiple parameters involved need to be tuned before application, which is unattainable for non-experts although some algorithms for optimizing parameters have been applied [54; 55]. Deep learning algorithms use a more complex modeling architecture consisting of convolutional, activation, pooling, and fully connected layers, taking images as input parameters. Feature selection and information filtration are performed in the pooling layer, which is a robust step in CNN. Importantly, the dimensionality reduction is performed without changing the depth of the maps. Thus, deep learning algorithms have the ability to process data efficiently, feature extraction of high-dimensional data, and keep high prediction accuracy. The accuracy of LSM should not be the only priority. Identifying the major conditions that lead to landslides is also critical, which assists in furthering the process [56; 57]. Identifying subjective weights and objective weights allows for separating their contributions. Analytic hierarchy processes (AHP) and factor analyses (FA) are commonly used methods [58; 59]. Landslides and their underlying conditioning factors can be directly correlated using LR models [60; 61]. The relative importance of factors can be determined by the magnitude, plus, or minus of the coefficients. In addition, bivariate methods are capable of distinguishing factors with different susceptibilities to landslides across interval ranges. Accordingly, LR and IV are recommended to be combined to analyze the factor's contribution to a landslide occurring. GI describes the contribution of the conditioning factors by calculating the total decrease, and the final coefficients reflect the relative importance of different factors. Some studies have applied GI for feature selection, which helps to decrease redundant information [62; 63]. As for bivariate methods such as IV, FR, and CF, the performance is mainly up to whether the division of the conditioning factors interval is reasonable. Some factors such as DTF and DTR in the study may show a logical distribution while the NDVI showed a chaotic distribution. However, there is no consensus on the size or number of intervals, although some methods such as natural breaks and equal intervals have been applied.