## 4 Evaluation Method
### Comprehensive Evaluation and Decision Analysis
#### 4.2.6 Bayesian Network Model (BNM)  
The BNM serves as a statistical modeling instrument rooted in the principles of probability and graph theory. It consists of a directed acyclic graph coupled with a conditional probability table, both of which illustrate the conditional relationships among random variables in intricate systems. BNM is notably proficient in calculating posterior probability distributions for unobserved variables, integrating both quantitative and qualitative data into a conditional probability structure based on the observed variables (for instance, variables may include Boolean (yes/no), qualitative assessments (low/medium/high), or continuous measurements). This ability to represent different types of variables is an essential characteristic of BNs, which motivates our use of this methodology to assess system resilience [137]. In GSE, the BNM is utilized to anticipate possible alterations in suitability with varied input parameters, thus offering a scientific basis for the judicious development and use of underground environments. The BNM develops a structural framework to illustrate the relationships among various variables. It sets up prior and conditional probabilities grounded in geological survey data, subsequently using Bayesian inference to calculate the posterior probabilities of suitability for each assessment area across diverse categories. Upon completing the modeling process, it is essential to create the conditional probability table for the nodes. The first approach, data-driven training, is suitable for all measurable objective variables and necessitates training on comprehensive datasets. In contrast, the second approach depends on expert survey, facilitating the amalgamation of multi-source data [138] and the determination of the weight of each factor at every level through the AHP judgment matrix. The BNM present a range of benefits, such as robust data mining functionalities, the combination of expert insights with data to navigate intricate relationships of uncertainty, and an intuitive framework for probabilistic reasoning and decision-making support [139; 140; 141]. Moreover, the BNM is capable of illustrating causal interactions between evaluation metrics and objectives [137; 138]. Nevertheless, developing these models requires considerable historical data as well as specialized knowledge. In addition, they can be vulnerable to issues related to incomplete data and uncertainty, and the intricacies and computational demands associated with them may present obstacles in real-world applications [139] (Figure 7). In GSEUUS within Changsha [129], [PERSON] et al. meticulously formulated a system of indices that encompasses factors, like formation type, groundwater level, and soil strength, drawing on the relationships among these input variables. To support this analysis, a Bayesian network framework was established. Through the application of this Bayesian network model, the research provides a scientific basis for the prudent planning and development of subterranean space in Changsha City, thereby improving the accuracy and scientific integrity of urban planning decisions (Figure 8e and Table 1). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Time of Publication** & **Author** & **City** & **Evaluation Index** & **Weight Evaluation** & \begin{tabular}{c} **Comprehensive** \\ **Evaluation and** \\ **Decision Analysis** \\ \end{tabular}  & **Evaluation System** & **Notes** \\ \hline
2024 & [PERSON], [PERSON], [PERSON] & \ \begin{tabular}{c} \\ \end{tabular}  & Foshan City & \begin{tabular}{c} Existing ground restrictions, the \\ structures on the ground, \\ engineering geological and \\ hydroological conditions \\ \end{tabular}  & EWM, GI method & GRA & & \\ \hline  
2022 & [PERSON], [PERSON], [PERSON] &
\begin{tabular}{c}