## 3 Results
### Prediction Results and Analysis of Water Quality Parameters  
As can be seen from Table 3, the PCWA-ResCNN model has the best fitting characteristics, and the R\({}^{2}\) of TUB, CODMn, NH\({}_{3}\)-N, and DO parameters are 97.92%, 95.29%, 95.01%, and 96.12%, respectively, showing satisfactory prediction ability. Compared with the traditional CNN model, the prediction accuracy of PCWA-ResCNN improved by 4.16%, 8%, 8.58%, and 7.09%, respectively. The LSTM model was not effective in predicting TUB, CODMn, and NH\({}_{3}\)-N, with R\({}^{2}\) of 84.99%, 77.9%, and 68.42%, respectively. However, the prediction effect of the DO parameter was poor, and the R\({}^{2}\) value was only 33.87%. The experimental results show that the LSTM model is not suitable for predicting DO parameters in the Yellow River basin of Ningxia. The change in water quality in the Yellow River basin involves multi-scale and nonlinear factors. The LSTM model mainly focuses on the long-term and short-term dependence of time series data, and the processing ability of these multi-scale and nonlinear factors is limited. The mixed model of deep learning can be used to consider the multi-dimensional characteristics and complexity of the data to improve the prediction effect.  
In terms of RMSE, MAPE, and RPD, the PCWA-ResCNN model has excellent performance on all parameters. This fact is reflected in its smaller RMSE and MAPE, in addition to its higher RPD. These experimental results further validate the superiority of the PCWA-ResCNN model in water quality parameter prediction.  
The scatter diagram of observed and predicted values of water quality parameters by the PCWA-ResCNN, CNN, and LSTM models is shown in Figure 4. These scatter plots clearly show the relationship between the predicted and observed values of each model. By observing these scatter plots, it can be seen that the PCWA-ResCNN model shows excellent performance in predicting water quality parameters, regardless of the R\({}^{2}\), RMSE, MAPE, or RPD indicators, which is far superior to the traditional CNN model and LSTM model. The PCWA-ResCNN model performs well in processing complex data structures and feature extraction. The R\({}^{2}\) values of the four water quality parameters of TUB, CODMn, NH\({}_{3}\)-N, and DO all exceed 0.95, showing strong robustness and an excellent feature extraction ability. The model can capture local and global information effectively, and it has a satisfactory spatial feature learning ability. Therefore, it is recommended to use the PCWA-ResCNN model to predict and analyze water quality parameters in practice in order to obtain more reliable and accurate results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Water Quality Parameter** & **Model** & **R\({}^{2}\)\(\uparrow\)** & **RMSE\(\downarrow\)** & **MAPE\(\downarrow\)** & **RPD\(\uparrow\)** \\ \hline \multirow{3}{*}{TUB (NTU)} & PCWA-ResCNN & 0.9792 & 80.4109 NTU & 2.5953 & 7.0347 \\  & CNN & 0.9376 & 139.2386 NTU & 3.1927 & 4.0626 \\  & LSTM & 0.8499 & 215.8538 NTU & 3.4165 & 2.6206 \\ \hline \multirow{3}{*}{CODMn (mg/L)} & PCWA-ResCNN & 0.9529 & 0.3005 mg/L & 0.0876 & 4.6780 \\  & CNN & 0.8729 & 0.4938 mg/L & 0.1549 & 2.8474 \\  & LSTM & 0.7790 & 0.6512 mg/L & 0.1941 & 2.1591 \\ \hline \multirow{3}{*}{NH\({}_{3}\)-N (mg/L)} & PCWA-ResCNN & 0.9501 & 0.0090 mg/L & 0.2291 & 4.5441 \\  & CNN & 0.8643 & 0.0149 mg/L & 0.3120 & 2.7550 \\  & LSTM & 0.6842 & 0.0227 mg/L & 0.5212 & 1.8063 \\ \hline \multirow{3}{*}{DO (mg/L)} & PCWA-ResCNN & 0.9612 & 0.1997 mg/L & 0.0188 & 5.1517 \\  & CNN & 0.8903 & 0.3356 mg/L & 0.0369 & 3.0651 \\ \cline{1-1}  & LSTM & 0.3387 & 0.8241 mg/L & 0.0897 & 1.2482 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Precision analysis statistics of PCWA-ResCNN, CNN, and LSTM models.