## 3 Results  
### Prediction Results  
The continuous monthly runoff data were input into the model to gain the runoff prediction results from different models, as shown in Figure 6. To better analyze the eigenvalues of each model, the relevant data were calculated and plotted in Table 1.  
For the average value, the observed monthly average runoff depth is 67.25 mm. The monthly average runoff depth simulated by the MMM model is 65.75 mm, which is the closest to the observed value, proving that the MMM model shows a promising prediction in this regard. The monthly average runoff depth predicted by the SVM model and ENN model is 61.36 mm and 70.15 mm, respectively. It can be seen that the predicted values of the SVM model and ENN model are different from the actual observed values, and the prediction results are not as outstanding as the MMM model. Figure 5: Data structure. The minimum value of the measured runoff was 1.51 mm, while the minimum values of the runoff predicted by the model are negative. In terms of minimum prediction, the SVM model, ENN model, and MMM model need to be improved to ensure that the data are all positive. To further analyze the maximum monthly runoff, the runoff data graphs were extracted and the details are shown in Figure 6. For the maximum monthly runoff, the observed maximum monthly runoff is 440.57 mm. As shown in Figure 7, the maximum runoff predicted by the SVM, MMM, and ENN models were 253.28 mm, 264.18 mm, and 234.41 mm, respectively, with absolute errors of 187.29 mm, 176.39 mm, and 206.16 mm, obtained at sequence numbers of 128, 103, and 127. The predicted value of the SVM model is closest to the observed value, indicating a better prediction. In terms of time, the maximum monthly runoff predicted by the SVM model and the observed maximum monthly runoff is the same month, again indicating that the SVM model predicts better. The maximum monthly runoff predicted by the ENN model and the maximum observed monthly runoff occurred 25 months apart. In this respect, ENN had the worst prediction. Meanwhile, the maximum monthly runoff predicted by the MMM model differs from the observed maximum monthly runoff by only one month on the time scale. To further analyze the prediction performance, the absolute error (AE) was calculated, and the results are shown in Figures 8-10. A positive AE value means that the predicted value is higher than the observed value, which means that the predicted runoff value is larger than the actual one. A negative AE value means that the predicted value is lower than the observed value, which means that the predicted amount of water is smaller than the actual amount. For the SVM model, 64 of the AE values were positive, accounting for 48.48% of the total data, while the ENN model and the MMM model had more than half of the positive AEs, reaching 59.85% and 56.06%, respectively. A positive AE value indicates that the predicted runoff depth is on the high side. The larger the predicted flow depth is, the safer it is for engineering designs (such as reservoir projects and bridge projects). From the above engineering safety point of view, the ENN model embodies a good performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Average Value (mm)**} & **Minimum Value** & **Maximum Value** \\  & & **(mm)** & **(mm)** \\ \hline Observed model & 67.25 & 1.51 & 440.57 \\ SVM model & 61.36 & \(-\)4.18 & 253.28 \\ ENN model & 70.15 & \(-\)12.69 & 264.18 \\ MMM model & 65.75 & \(-\)2.23 & 234.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Monthly runoff characteristic values. Figure 6: Prediction results of SVM model, ENN model, and MMM model. Figure 8: AE of SVM model. Figure 7: The monthly runoff maximum. Figure 9: AE of ENN model. A box plot of absolute errors was drawn in order to further reveal comparisons among the SVM model, ENN model, and MMM model in Figure 11. Thus, a positive absolute error denotes an overestimation of an observed value.