# Water Quality Evaluation and Prediction Based on a Combined Model
## 4 Results and Discussion
### Water Quality Prediction Model
#### 4.2.3 Water Quality Prediction of Longdong Section  
In order to verify the predictive ability of the combined model proposed in this paper for different water systems, the water quality index data of the Longdong section of Panzhihua in Sichuan Province of the Yangtze River Basin are selected for prediction and analysis. The modeling results are as follows:  
\[P_{DO}=0.1044P_{WNN}+0.8956P_{ARIMA}\]  
\[P_{COD_{Mn}}=0.0432P_{WNN}+0.9568P_{ARIMA}\]  
\[P_{NH_{3}-N}=0.8646P_{WNN}+0.1354P_{ARIMA}\]  
The improvement in the prediction accuracy and combined model prediction ability of the single and combined models for each indicator are compared in Tables 8 and 9.  
Figure 16: Comparison of each model fitting for NH\({}_{3}\)-N.  
Figure 14: Comparison of each model fitting for dissolved oxygen.  
Figure 15: Comparison of each model fitting for CODMn.  
As shown in Table 8, the prediction effect of the combined model is better than that of each single model. For DO, COD\({}_{\text{Mn}}\) and NH\({}_{3}\)-N, the combinatorial model outperformed the ARIMA model by 2.99%, 6.73% and 68.06%, respectively, in terms of prediction ability and outperformed the prediction of the WNN model by, 58.33%, 66.17% and 11.67%, respectively.  
In the prediction of various indicators of the Longdong section of Panzhihua in Sichuan in the Yangtze River Basin, the bat algorithm still assigns more weight to the single model with better prediction ability and less weight to the single model with poor prediction ability. With respect to DO, the MAPE value predicted by the wavelet neural network is 4.68%, compared with 2.01% for the ARIMA model, which suggests that the ARIMA model achieves better predictive performed; therefore, in the combinatorial model, the bat algorithm assigns it a weight of 0.8956, whereas the WNN only assigns it a weight of 0.1044. In comparison with a BP neural network and LSSVM, the prediction accuracy of the combined model proposed in this paper is higher; the comparison results are shown in Figures 18-21.  
In the prediction of various water quality indices in the Longdong section of Panzhihua, Sichuan, in the Yangtze River Basin, the prediction effect of the combined model is better and the accuracy is higher than that of the single model, the BP neural network and the LSSVM, indicating that the combined model proposed in this paper is suitable for water quality prediction in different river basins, with satisfactory generalization performance [40].  
Figure 17: Comparison of the combined model with other models.  
Figure 19: Comparison of model prediction of CODMn.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Water Quality Indicator** & **Prediction Accuracy Indicator** & **ARIMA** & **WNN** & **Combined Model** \\ \hline \multirow{4}{*}{DO} & MAPE & 2.01\% & 4.68\% & 1.95\% \\  & MAE & 0.1860 & 0.4116 & 0.1815 \\  & RMSE & 0.2481 & 0.5201 & 0.2446 \\  & MAPE & 6.39\% & 17.62\% & 5.96\% \\ \multirow{4}{*}{CODMn} & MAE & 0.1169 & 0.3220 & 0.1045 \\  & RMSE & 0.1578 & 0.5055 & 0.1498 \\ \multirow{4}{*}{NH\({}_{3}\)-N} & MAPE & 27.02\% & 9.77\% & 8.63\% \\  & MAE & 0.0236 & 0.0092 & 0.0088 \\ \cline{1-1}  & RMSE & 0.0315 & 0.0144 & 0.0148 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of single and combined model prediction accuracy of each indicator.  
Figure 18: Comparison of model prediction of dissolved oxygen.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **DO** & **CODMn** & **NH3-N** \\ \hline AIARIMA & 2.99\% & 6.73\% & 68.06\% \\ AIWNN & 58.33\% & 66.17\% & 11.67\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Improvement in the predictive power of the combined model compared to a single model.