# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
## 5 Experimental Results and Discussion
### Comparative Experiments Results
(**f**) Fitted curves for all the models anchored at the origin (comparison with standard 45\({}^{\circ}\) line). Figure 9: Unit flow rate scatter plot: (**a**) GRU model predicted vs. actual unit flow rate (m\({}^{3}\)/s). (**b**) LSTM model predicted vs. actual unit flow rate (m\({}^{3}\)/s). (**c**) LSTM-GRU model predicted vs. actual unit flow rate (m\({}^{3}\)/s). (**d**) RS model predicted vs. actual unit flow rate (m\({}^{3}\)/s). (**f**) Fitted curves for all the models anchored at the origin (comparison with standard 45\({}^{\circ}\) line). The scatter plots in Figure 9a-e show the predicted unit flow rates using various models compared with the actual values, while Figure 9f presents a comparison of the fitted curves for all the models. The scatter plots (Figure 9a-e) demonstrate the predicted unit flow rates produced by each model against the actual measured values. In each subfigure, the 45\({}^{\circ}\) reference line (in black) represents the ideal scenario where the predictions perfectly match the actual values (i.e., zero prediction error). Points closer to this line indicate higher predictive accuracy, while points further from the line indicate increased error. As illustrated in Figure 9a-e, the scatter points generated by the proposed model (Figure 9e) are more densely clustered around the 45\({}^{\circ}\) line compared with the other models (GRU, LSTM, LSTM-GRU, and RS), indicating that the proposed model provides a more accurate prediction of the unit flow rate, with lower prediction errors. To further qualitatively compare the fitting performance across models, Figure 9f shows the fitted curves of the actual vs. predicted values for each model. All the fitted curves are anchored at the origin (\(0,0\)) to ensure a consistent starting point, providing a clearer comparison of how the models' performance diverges as the values increase. In Figure 9f, the proposed model's fitted curve shows minimal deviation from the 45\({}^{\circ}\) line, indicating strong prediction performance. In contrast, the other models (GRU, LSTM, and LSTM-GRU) exhibit greater deviations from the 45\({}^{\circ}\) line, reflecting relatively higher prediction errors. In Figure 10a-e, it is evident that the proposed model (Figure 10e) results in a denser clustering of points around the 45\({}^{\circ}\) line. This suggests that the proposed model offers higher predictive accuracy for guide vane opening compared with the other models (GRU, LSTM, LSTM-GRU, and RS). In Figure 10f, the angles between the fitted curves and the 45\({}^{\circ}\) line for all the models are compared. The fitted curve of the proposed model aligns more closely with the 45\({}^{\circ}\) line than those of the GRU, LSTM, and LSTM-GRU models, indicating better generalization ability and lower error across the entire guide vane opening prediction range. As shown in Figure 11a-e, the proposed model (Figure 11e) produces points that are more evenly distributed along the 45\({}^{\circ}\) line, with fewer outliers compared with the  
Figure 11: Maximum water guide tile temperature scatter plot (**a**) GRU model predicted vs. actual maximum water guide tile temperature (\({}^{\circ}\)C). (**b**) LSTM model predicted vs. actual maximum water guide tile temperature (\({}^{\circ}\)C). (**c**) LSTM-GRU model predicted vs. actual maximum water guide tile temperature (\({}^{\circ}\)C). (**d**) RS model predicted vs. actual maximum water guide tile temperature (\({}^{\circ}\)C). (**e**) Proposed model predicted vs. actual maximum water guide tile temperature (\({}^{\circ}\)C). (**f**) Fitted curves for all the models (comparison with standard 45\({}^{\circ}\) line). other models. While all the models generally align with the 45\({}^{\circ}\) line, the proposed model demonstrates a more balanced and uniform distribution of points, avoiding the clustering on one side of the line observed in the other models.