## 4 Evaluation Method
### Comprehensive Evaluation and Decision Analysis  
#### 4.2.1 Multi-Objective Linear Weighting (MOLW)  
MOLW serves as a technique for evaluation functions that allocates weight coefficients to each objective according to its relative importance, ultimately enhancing their linear combination to tackle multi-objective programming dilemmas. This strategy merges several objective functions into one single function through linear weighting, thus redefining the issue as a standard linear programming challenge. The MOLW is mainly utilized to confront multi-objective decision-making issues, especially in situations involving both linear and non-linear constraints aimed at fulfilling various optimal objective functions and decision variables [125]. It is particularly advantageous in cases where objectives resist expression in a common dimension and where it is challenging to set a uniform metric for assessing the quality of a plan. First, the various objective functions that need optimization are identified, followed by assigning weight coefficients based on the significance of each objective. Subsequently, each objective function is multiplied by its respective weight coefficient and aggregated to form the evaluation function. As a result, the multi-objective programming dilemma is converted into a numerical optimization task concerning the evaluation function, and suitable optimization algorithms are utilized to solve and interpret the results in order to uncover the ideal solution.  
The MOLW offers several key benefits, including its straightforward nature and clarity, ease of execution, and the capability to integrate all original data index variables, thus facilitating a thorough assessment. This technique converts various objective functions into a unified objective function via linear combination, enabling the use of established algorithms tailored for single-objective optimization challenges. Nonetheless, the multi-objective linear weighting method has its drawbacks. One major issue is that weight determination frequently depends on subjective assessments, which can introduce personal bias and undermine objectivity. Additionally, the linear weighting strategy might fail to adequately represent the considerable impact of specific evaluation metrics, especially when an evaluated entity performs exceptionally well in one metric but poorly in others. This shortcoming could hinder a comprehensive portrayal of the variations involved, ultimately influencing the precision of the evaluation outcomes. Moreover, the choice of weights is heavily reliant on the particular context of application, and the distribution of weights across different objective functions can result in inaccuracies within the reward function in certain cases (Figure 7).  
An evaluation index system was developed by researchers to examine the challenges associated with the development of shallow USR from various viewpoints, including geological and groundwater factors in Nantong City [106]. They used a MOLW to arrive at a comprehensive evaluation result. Similarly, the MOLW was applied in the ecological new city in Gui'an New Area [126]. A hierarchical evaluation system specific to sub-regions for UUS utilization was created, which assigned variable weights to elements, such as geological factors, groundwater vulnerability, and the current state of surface space utilization, ensuring an accurate representation of their importance. The adoption of the MOLW method in GSEUUS has shown significant effectiveness and practicality. Nevertheless, it is essential to carefully monitor the process of weight determination during implementation to maintain the objectivity and precision of the evaluation findings (Figure 8b and Table 1).  
Figure 7: Entity relationship diagram of main comprehensive evaluation and decision analysis methods.