## 2 Materials and Methods
### Technical Lines of Research  
This study introduces a collapse disaster risk assessment model that leverages convolutional neural network (CNN) technology to evaluate collapse disaster susceptibility in the Guangxi Zhuang Autonomous Region. The technical roadmap for this model is outlined in Figure 2. \begin{table}
\begin{tabular}{c c c c} \hline
**Data Type** & **Data Description** & **Data Sources** & **Website** \\ \hline \multirow{3}{*}{Topographic Data} & DEM data of & \multirow{3}{*}{Geospatial Data Cloud} & [[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/)) \\  & Guangxi Zhuang & & (accessed on 20 \\  & Autonomous Region & & December 2024) \\ \hline \multirow{3}{*}{Vegetation Coverage} & \multirow{3}{*}{National 1000 m resolution vegetation coverage data} & Institute of Geographic & \multirow{3}{*}{[[http://www.resdc.cn/](http://www.resdc.cn/)]([http://www.resdc.cn/](http://www.resdc.cn/)) (accessed on 20 \\  & National 1000 m resolution vegetation coverage data & Sciences and Natural Resources Research, Chinese Academy of Sciences & December 2024) \\ \hline \multirow{3}{*}{Road Data} & Highway distribution data of Guangxi and surrounding areas & National Geographic & \multirow{3}{*}{[[https://www.webmap.cn/](https://www.webmap.cn/)]([https://www.webmap.cn/](https://www.webmap.cn/)) (accessed on 20 \\  & Population distribution & Information Resource & (accessed on 20 \\  & data of Guangxi Zhuang & Directory Service System & December 2024) \\ \hline \multirow{3}{*}{Population Data} & Population distribution & LandScan & \multirow{3}{*}{[[https://landscan.ornl.gov/](https://landscan.ornl.gov/)]([https://landscan.ornl.gov/](https://landscan.ornl.gov/)) (accessed on 20 \\  & Autonomous Region & Population Dataset & (accessed on 20 \\  & & & December 2024) \\ \hline \multirow{3}{*}{Precipitation Data} & Monthly average precipitation data of Guangxi (2019-2023) & National Tibetan Plateau & \multirow{3}{*}{[[https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)]([https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)) (accessed on 20 \\  & Lithological distribution & Data Center & (accessed on 20 \\  & data of Guangxi Zhuang & Information Resource & (accessed on 20 \\  & Autonomous Region & Directory Service System & December 2024) \\ \hline \multirow{3}{*}{Geological Hazard Distribution Data} & \multirow{3}{*}{Vector data of geological hazard points in Guangxi} & Institute of Geographic & \multirow{3}{*}{[[http://www.resdc.cn/](http://www.resdc.cn/)]([http://www.resdc.cn/](http://www.resdc.cn/)) (accessed on 20 \\  & hazard points in Guangxi & Chinese Academy & December 2024) \\ \hline \end{tabular}
\end{table} 
Table 1: Data fact sheet. Figure 2: Technology roadmap of the collapse disaster susceptibility risk assessment model. This figure is an original illustration created by the authors of this study and is protected by copyright © 2025. The development of the model follows a four-step technical framework:  
1 Data Collection: Multi-source data pertinent to collapse disasters are compiled, including historical collapse disaster inventory data, geological conditions (e.g., lithology, slope, and aspect), natural geographic factors (e.g., precipitation), and anthropogenic factors (e.g., road networks and land use). These data are preprocessed through standardization, projection transformation, and rasterization to create an analysis dataset tailored for the CNN model [7]. 2 Model Training and Optimization: The CNN model is trained using the preprocessed dataset, with fivefold cross-validation applied to optimize hyperparameters. The rectified linear unit (ReLU) activation function and the Adam optimizer are employed to enhance training efficiency and predictive accuracy [14]. 3 Susceptibility Prediction: The trained CNN model is used to predict susceptibility levels across Guangxi, integrating key factors such as slope, lithology, and distance from roads [15].