## 4 Evaluation Method
### Weight Calculation Method
#### 4.1.2 Analytic Hierarchy Process AHP  
The AHP [107] serves as a method for quantitative analysis aimed at tackling intricate decision-making challenges. The AHP breaks the issue down into several tiers by creating a decision-making framework and determining the weight of each evaluation criterion via mathematical techniques. When confronted with uncertainty and ambiguity, AHP adeptly merges expert subjective evaluations with quantitative analysis, ultimately helping decision-makers make more informed selections. The method considers the influence of various elements on the evaluation outcomes, presenting notable benefits for dealing with such issues [108]. The fundamental concept of AHP can be encapsulated by the phrase \"first decompose, then synthesize.\" Employing AHP in decision-making comprises several essential stages: (1) Developing a hierarchical framework; (2) Creating a decision table for program factors; (3) Standardizing to create a judgment matrix; (4) Conducting a consistency check on the judgment matrix; (5) Determining the weights derived from the judgment matrix; (6) Computing and ranking the overall weights. The AHP technique is widely utilized in various multi-factor suitability evaluations and complex system risk assessments within engineering project risk management [109; 110].  
The AHP presents various benefits that improve the transparency of decision-making by breaking down complex issues into different hierarchical levels, thus clarifying all relevant facets of the problem. It merges quantitative assessments with qualitative evaluations, permitting a thorough examination of diverse factors and rendering it appropriate for an extensive array of decision-making situations. AHP also offers a framework for evaluating the consistency of judgments, which aids in ensuring the rationality and precision of the decision-making process. Moreover, it accommodates multiple criteria and alternatives, making it relevant to various intricate decision-making scenarios. Thanks to its organized method, AHP is straightforward to explain and share, promoting teamwork and collaborative decision-making. Nonetheless, it is crucial to recognize that AHP is dependent on the subjective evaluations of the decision-maker, which could introduce bias and inconsistency, especially within the judgment matrix [111]. For issues with more complex hierarchies, the computation process may become laborious, particularly when a large number of comparisons is necessary. Although AHP features a mechanism for checking consistency, achieving consistent judgments remains challenging in real-world applications, especially when faced with a substantial number of assessments. Furthermore, the scales utilized in AHP might affect the precision of the outcomes, particularly if decision-makers have differing interpretations of these scales. In certain cases, decision-makers may find it difficult to accurately assign weights, resulting in less dependable final results (Figure 5).  
Figure 5: Entity relationship diagram of main weight evaluation methods.  
The AHP is the most widely utilized method for GSEUUS. It can be employed independently to determine indicator weights [112; 113] and can effectively be combined with other methodologies. This integration aims to diminish the influence of subjective judgment on evaluations and enhance their accuracy. For instance, [PERSON] and [PERSON] [114] combined AHP with the most unfavorable grading method (MUGM) and the exclusive method (EM) technologies to perform a comprehensive quality assessment of urban construction suitability and resources in Tongren and Changzhou cities in China. Similarly, [PERSON] et al. [11] evaluated the overall quality of UUS in Foshan, China, employing AHP alongside variable fuzzy set theory, and a 3D geological model (Figure 6a-c and Table 1).