###### Abstract  
Precisely assessing the vulnerability of landslides is essential for effective risk assessment. The findings from such assessments will undoubtedly be in high demand, providing a solid scientific foundation for a range of critical initiatives aimed at disaster prevention and control. In the research, authors set the ancient core district of Sichuan Moxi Ancient Town as the research object; they conduct and give the final result of the geological survey. Fault influences are commonly utilized as key markers for delineating strata in the field of stratigraphy, and the slope distance, slope angle, slope aspect, elevation, terrain undulation, plane curvature, profile curvature, mean curvature, relative elevation, land use type, surface roughness, water influence, distance of the catchment, cumulative water volume, and the Normalized Vegetation Index (NDVI) are used along roads to calculate annual rainfall. With the purpose of the establishment of the evaluation system, there are 17 factors selected in total. Through the landslide-susceptibility assessment by the coupled models of DNN-I-SVM and DNN-I-LR nine factors had been selected; it was found that the Area Under the Curve (AUC) value of the Receiver Operating Characteristic Curve (ROC) was high, and the accuracy of the model is relatively high. The coupler, DNN-I-LR, gives 0.875 of an evaluation accuracy of AUC, higher than DNN-I-SVM, which yielded 0.860. It is necessary to note that, in this region, compared to the DNN-I-SVM model, the DNN-I-LR coupling model has better fitting and prediction abilities.  
landslide; deep learning; causative factor; susceptibility assessment; coupled modeling +
Footnote †: journal:  
In the realm of landslide susceptibility evaluation, models are categorized based on the diverse methodologies employed, which theoretically encompass probabilistic, heuristic, deterministic, mathematical-statistical, and machine learning approaches, as informed by landslide inventories [6; 7]. Among these, certain models are frequently cited in scholarly research, including Random Forest, Artificial Neural Networks, and Decision Trees. This diversity suggests that the occurrence of landslides is influenced by a multitude of factors, and it is unlikely that all contributing elements are as yet identified. On one hand, the complexity of the interrelations among various factors necessitates the consideration of numerous evaluation criteria to establish a robust susceptibility framework [8; 9]. However, certain limitations inherent in machine learning models, as previously noted, may obscure these correlations and fail to yield optimal outcomes [10; 11]. This challenges the abstractions derived from earlier machine learning models, prompting a shift towards deep learning algorithms, which are perceived to offer greater accuracy [12]. Among the commonly applied deep learning algorithms are Deep Neural Networks (DNNs), which consist of multiple hidden layers and have been widely recognized for their efficacy across various domains, producing significant results [13]. In the context of susceptibility evaluation, the models that have been employed include the Information (I) model, the Support Vector Machine (SVM) model, and the Logistic Regression (LR) model. These models are often combined to leverage their individual strengths and achieve more precise evaluative outcomes [14; 15]. The integration of such models reflects a sophisticated approach to hazard assessment, aiming to distill a comprehensive understanding of the factors that contribute to landslide susceptibility.