## 3 Algorithm Improvement and Scheme Evaluation
### Improvement Strategy for the NSGA-III Algorithm
#### 3.1.4 Algorithm Implementation Process:
Over-extraction has resulted in the formation of the Jiexiu-Songgu funnel area, exacerbating the conflict between water supply and demand. There is an urgent need to optimize the water supply structure and distribution in Jinzhong City to conserve groundwater resources and support its high-quality development. For research purposes, the area is divided into seven water supply sub-regions according to administrative divisions. Figure 2 presents the structure of the water supply system. ### Data Source

According to the 2016-2020 Statistical Bulletin of National Economic and Social Development of Jinzhong City, population data were obtained. Data on effective irrigation area, added value of various industries, and per capita green space area were sourced from the Jinzhong Statistical Yearbook. Information on water supply and water usage by different industries was derived from the Shanxi Province Water Resources Bulletin and the Jinzhong Statistical Yearbook. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **First-Level** \\ **Indicator** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **First-Level** \\ **Indicator Weight** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Second-Level Indicator** \\ **AHP** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **Entropy Weight** \\ **Method** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Comprehensive** \\ **Weight** \\ \end{tabular} } \\ \hline \multirow{2}{*}{Economic} & \multirow{2}{*}{\(1/3\)} & per capita GDP & 0.47 & 0.29 & 0.38 \\  & & Proportion of secondary industry & 0.3 & 0.28 & 0.29 \\  & & Proportion of tertiary industry & 0.23 & 0.43 & 0.33 \\ \hline \multirow{2}{*}{Social} & \multirow{2}{*}{\(1/3\)} & per capita water supply & 0.56 & 0.64 & 0.60 \\  & & Water shortage rate & 0.44 & 0.36 & 0.40 \\ \hline \multirow{2}{*}{Ecological} & \multirow{2}{*}{\(1/3\)} & per capita pollutants Emissions & 0.5 & 0.29 & 0.39 \\  & & Ecological water shortage rate & 0.5 & 0.71 & 0.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Indicator weight of multi-system coupling coordination degree evaluation model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Coordination Degree** & **Coordination Stage** & **Coordination Degree** & **Coordination Stage** \\ \hline
[0.0, 0.1) & Extreme Imbalance & [0.5, 0.6) & Barely Coordinated \\
[0.1, 0.2) & Severe Imbalance & [0.6, 0.7) & Primary Coordination \\
[0.2, 0.3) & Moderate Imbalance & [0.7, 0.8) & Intermediate \\
[0.3, 0.4) & Mild Imbalance & [0.8, 0.9) & Good Coordination \\
[0.4, 0.5) & Borderline Imbalance & [0.9, 1.0) & High-Quality \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Coordination evaluation standard of multi-system. ### Water Supply and Demand Forecast

This study uses 2020 as the baseline year, with 2030 and 2035 as planning years, and selects normal (with a water inflow frequency of P = 50%) and drought scenarios (P = 75%) for the optimized allocation of water resources. Based on Jinzhong City's future development plans, this study utilizes data from the _Shanxi Province Water Resources Bulletin, Shanxi Province Water Use Quotas, Jinzhong City's 2020 National Economic and Social Development Statistical Bulletin_, and _Jinzhong Statistical Yearbook_ to obtain information on current water use and economic and social development indicators. Using the quota method and trend analysis method, the water demand for each user in Jinzhong City during the planning level year is analyzed, and the Tennant method is used to calculate the ecological water demand within the river channels. The total water demand for 2030 in the normal and drought scenarios is calculated to be 71,903 \(\times\) 10\({}^{4}\) m\({}^{3}\) and 77,796 \(\times\) 10\({}^{4}\) m\({}^{3}\), respectively, while the total water demand for 2035 in the normal and drought scenarios is 66,196 \(\times\) 10\({}^{4}\) m\({}^{3}\) and 73,282 \(\times\) 10\({}^{4}\) m\({}^{3}\).