## 4 Uncertainty Analysis
### Transferability  
In some regions, the scarcity of landslide samples makes it difficult to use models for landslide susceptibility assessment. Scholars have attempted to directly extend the models from one region with landslides to areas lacking landslide samples but found the results to be ineffective. For instance, [PERSON] et al. [137] found that machine learning-based landslide susceptibility assessment models, when applied outside the study area, exhibited significantly reduced accuracy and lacked good transferability. This can be attributed to spatial heterogeneity. In cases of spatial heterogeneity, the relationship between disaster-driving factors (dependent variables) and disaster variables (independent variables) may vary in adjacent spatial regions. Therefore, it is necessary to consider the heterogeneity of driving factors when analyzing landslide susceptibility [138]. In large-scale study areas, considering spatial heterogeneity is necessary for achieving higher accuracy in landslide susceptibility assessment. However, for regional studies, this may be disregarded [139]. [PERSON] et al. [135] confirm the high spatial heterogeneity in landslide susceptibility and internal driving factors on the southern and northern slopes of the Himalayan region. The spatial heterogeneity of landslide evaluation factors is the main reason for the poor generalization ability of evaluation models. However, these models demonstrate better generalization ability in regions with similar regional characteristics [2]. Therefore, for regions with lower research levels, it may be considered appropriate to search for areas with similar terrain, geology, meteorology, vegetation, and other characteristics in places where in-depth research has already been conducted and apply the model to evaluate those areas. [PERSON] et al. [140] created a landslide inventory for the Da Bac region in Vietnam covering the period from 2013 to 2020, which included significant landslide events in 2018 and 2019. The authors conducted a landslide susceptibility assessment using logistic regression and support vector machine models under two different data application modes. The first data mode involved using landslide data spanning the entire period (2013-2020), while the second data mode involved training the model using landslide data from 2018 and validating it using data from 2019. The results showed that: (1) under both data modes, the support vector machine model outperformed the logistic regression model in terms of evaluation accuracy; (2) the choice of different datasets had little impact on the evaluation results, indicating that landslide susceptibility assessment using limited datasets is feasible; (3) landslide susceptibility assessment models trained on representative terrain conditions can be applied to regions with similar terrain features. [PERSON] et al. [141] divided Yunyang County into four zones, based on their geological properties, and constructed five models for the entire region and its four subzones. The results showed that the models for the subzones exhibited better performance. Therefore, training models in locations with similar geological properties can yield predictions with higher accuracy. How to establish an accurate, interpretable, and generalizable machine learning model under conditions of small sample sizes is a question worth researching in the future [2]. [PERSON] et al. [142] proposed a landslide susceptibility assessment method called the deep autoencoder with multi-scale residual convolutional neural network (DAE-MRCNN) to address issues such as insufficient landslide samples and the inadequate representation of nonlinear relationships among the evaluation factors. The authors applied this method to assess landslide susceptibility in Hanzhong City, Shaanxi Province, and compared it with three other methods (SVM, CPCNN, and 2D-CNN).