## 3 Results and Discussion
### LID Performance Evaluation and Sorting Optimization Based on TOPSIS  
In the results of the parameter sensitivity analysis, the structural parameters that have the most significant impact on the concave herbaceous field have been obtained. In order to determine the optimal choice of benefits among the different schemes formulated according to parameter changes, the TOPSIS optimal solution ranking method and Formulas (12)-(17) were used to further sort and comprehensively optimize the benefit results generated by the numerical simulation of each scheme. The TOPSIS analysis results of three kinds of runoff control benefits based on the disturbance change of flood peak reduction rate, flood peak time delay rate, and total overflow reduction rate are shown in Tables 4-6. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Disturbance Ratio**} & **Positive Ideal Solution** & **Negative Ideal Solution** & **Relative Proximity C** & **Sort Result** \\  & **Distance D+** & **Distance D–** & **Relative Proximity C** & **Sort Result** \\ \hline \(-30\%\) & 0.311 & 0.281 & 0.475 & 2 \\ \(-20\%\) & 0.406 & 0.308 & 0.432 & 4 \\ \(-10\%\) & 0.387 & 0.300 & 0.436 & 3 \\ \(0\%\) & 0.450 & 0.223 & 0.331 & 6 \\ \(10\%\) & 0.366 & 0.211 & 0.366 & 5 \\ \(20\%\) & 0.452 & 0.192 & 0.298 & 7 \\ \(30\%\) & 0.233 & 0.487 & 0.677 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Flood peak reduction rate corresponds to the TOPSIS analysis results of parameter disturbance change. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Indicator**} & **Mean Value** & **Standard Deviation** & **Coefficient of Variation** & **Weight** & **Composite Coefficient** \\ \hline A & 0.644 & 0.194 & 30.18\% & 6.47\% & 0.2274 \\ B & 1.078 & 1.320 & 122.37\% & 26.25\% & 0.7536 \\ C & 0.700 & 0.839 & 119.94\% & 25.73\% & 0.4613 \\ D & 0.727 & 0.623 & 85.66\% & 18.37\% & 0.2008 \\ E & 0.366 & 0.396 & 108.06\% & 23.18\% & 0.0848 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Information weight method calculation results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Disturbance Ratio**} & **Positive Ideal Solution** & **Negative Ideal Solution** & **Relative Proximity C** & **Sort Result** \\  & **Distance D+** & **Distance D–** & **Relative Proximity C** & **Sort Result** \\ \hline \(-30\%\) & 0.113 & 0.029 & 0.205 & 5 \\ \(-20\%\) & 0.134 & 0.009 & 0.062 & 7 \\ \(-10\%\) & 0.072 & 0.113 & 0.610 & 1 \\ \(0\%\) & 0.130 & 0.010 & 0.069 & 6 \\ \(10\%\) & 0.105 & 0.035 & 0.250 &The results show that, for flood peak reduction rate, when the parameter disturbance rate is \(30\%\), \(-30\%\), and \(-10\%\), the concave green space benefit ranking is better. In terms of the time delay rate of the flood peak, the efficiency order is the best when the disturbance rate is \(-10\%\), \(30\%\), and \(20\%\). From the perspective of the total flow reduction rate, the efficiency of the green space under the disturbance rate of \(-30\%\), \(20\%\), and \(10\%\) is more advanced. For the first case, the reason for this may be that, when there is a large planting soil thickness and aquifer height or a small planting soil slope, the rainwater runoff collected by the road surface is more likely to be absorbed and retained by the planting soil layer, resulting in a significant reduction of the maximum runoff generated at the drainage outlet, thereby improving the efficiency of flood peak reduction. In the second case, when the thickness parameters of the aquifer and the planting soil are more balanced or both are higher, the runoff generated in the early precipitation process will be able to remain in the aquifer and planting soil for a longer time, thus significantly delaying the generation time of runoff and flood peak caused by rain peak. In addition, in the third case, the reduction effect of total discharge may be more closely related to the slope of the planted soil.