## 4 Evaluation Method
### Comprehensive Evaluation and Decision Analysis
#### 4.2.2 Grey Relational Analysis (GRA)  
The GRA serves as a quantitative method for analyzing and comparing the developmental dynamics and changes within a system. It evaluates the degree of similarity between a continuous sequence and a reference sequence to establish the connection between the reference data sequence and several comparative data sets. The similarity in the shape of these sets is leveraged to assess how closely they are related, which reflects the correlation level between the curves and facilitates the establishment of spatial variable weights. A sequence that closely matches the reference data indicates a higher affiliation with that particular level, leading to an increased correlation coefficient [127]. In the framework of GSEUUS, the GRA method proves particularly effective in determining correlation coefficients among a variety of indicators. By multiplying the correlation coefficients obtained from different indicators with the predefined spatial variable weights, the criterion for maximum correlation can be utilized to evaluate the suitability of each unit within the 3D geological model [111]. The GRA evaluation process involves several steps: initially, recognizing the parent sequence that represents the behavioral characteristics of the system alongside the sub-sequences comprising factors that impact this behavior; thereafter, performing data preprocessing to lessen the influences of varying dimensions and data scales; following this, calculating the correlation coefficient by identifying the minimum and maximum differences at two levels and incorporating these figures into the correlation coefficient equation; next, deriving the correlation degree, which is the average of the correlation coefficients, to indicate the relationship between every sub-sequence and the parent sequence; finally, organizing or thoroughly evaluating based on the correlation degree to determine each factor's impact on the target factor.  
Figure 8: Application of main comprehensive evaluation and decision analysis: (a) [70]; (b) [106]; (c) [128]; (d) [51]; (e) [129]; (f) [67].  
GRA serves as a method for data evaluation that proficiently handles systems marked by a lack of complete information. The main benefit of this approach is its capability to navigate issues related to limited data sizes and considerable uncertainty. By computing correlations among sequences, this method uncovers the dynamic interrelations between variables, making it especially appropriate for situations with small sample sizes and insufficient information systems. Nonetheless, the correlation computation can be affected by subjective elements, including the choice of resolution coefficients, and necessitates comprehensive preprocessing of data sequences. Different preprocessing techniques may result in significant variations in the outcomes [48] (Figure 7).  
In Nanjing, researchers employed the GRA along with the EWM and an interval continuous mathematical model to determine evaluation indicators and compute their corresponding weights [130]. Furthermore, the GRA can improve the technique for order preference by similarity to ideal solution (TOPSIS). The revised TOPSIS model maintains the advantages of enhanced objectivity while utilizing gray correlation theory, especially in situations where information is scarce [52]. In Foshan, the enhanced GRA was utilized to transform the reference sequence into a reference interval and to redefine the correlation coefficient function, thereby facilitating a more accurate evaluation of each assessment unit's suitability [74]. This implementation not only increases the evaluation accuracy but also makes GRA more effective and practical for GSEUUS. (Figure 8d and Table 1)