## 5 Discussion
### Similarities and Differences  
The prediction results of the standard AI models show that BP performs better than Elman, which is consistent with the results of previous studies. In a study of the hydrology field, [PERSON] et al. [47] studied the flood prediction power of the model in the Longquan Creek watershed. The results showed that the BP model and the Elman model provided the best and the worst performance, respectively, for flood prediction in Longquan basin under the same prediction lead time. The BP model has also demonstrated more accurate predictions than the Elman model in other fields, such as power system protection and control. The outcome of the research conducted by [PERSON] et al. shows that the starting criteria for a fault-recorder algorithm based on the BP neural network can effectively complete the recording start, with minor prediction error [48]. In each of these studies, BP showed better predictions than Elman. After coupling the EMD and EEMD pre-processing techniques with the standard AI models to form double-coupled models, the predictions were significantly better than from the unmodified models. Similar results were also found by [PERSON] et al. [49]. They compared the performance of the constructed EEMD-AR model, EMD-AR model, and unmodified AR model using runoff data from four hydrological stations in the upper Fen River basin, China. The results showed that the hybrid EEMD-AR model was more accurate in predicting runoff. This phenomenon may be caused by the fact that the data pre-processing technique transforms the original series into multiple relatively stationary series, and thus can effectively reduce the impact of the nonlinear and nonstationary characteristics. Therefore, when making runoff predictions, preprocessing can be performed on the runoff series to fully exploit the limited sample data and extract meaningful information reflecting changes in the series, and then coupled with a single model, thus improving the accuracy of runoff prediction. Ensemble empirical modal decomposition can better solve the modal confounding problem in the empirical modal decomposition method by taking advantage of the good zero-mean characteristic of white noise. [PERSON] et al. [18] proposed the EMD-ANN and the EEMD-ANN models for long-term runoff prediction and evaluated the prediction accuracy of both; they found that, compared with the EMD-ANN model, the EEMD-ANN model performed better in predicting high flow values, and best matched the observed results. In the present study, the coupled models formed by EMD with Elman and BP were more accurate than the coupled models formed by EEMD with Elman and BP, which is inconsistent with some scholars' studies [18; 50]. This phenomenon may be due to the fact that the runoff series in the present study are based on karst areas, where the subterranean topographical conditions are extremely different from other areas, and which have a continuous effect on runoff [17]. Among three of the evaluation indicators employed in the present study, better model simulation is indicated by higher values for the Nash-Sutcliffe efficiency, and lower values for the root mean square error and the mean absolute percentage error [51]. The prediction results of the QM-EMD-Elman model showed a significant improvement in the MAPE and RMSE indicators compared with the EMD-Elman model, with only a slight fluctuation in the NSE indicator. This indicates that the prediction accuracy of the triple-coupled model, formed by adding post-processing to the double-coupled model, was improved again, suggesting that the post-processing method can effectively control the prediction error. This result is consistent with the study conducted by [PERSON] et al. [52] In this study, [PERSON] used the Xianjiang model driven by seven control forecast products, such as ECMWF and UKMO, for integrated runoff prediction and then investigated the effects of data processing schemes on runoff prediction accuracy and uncertainty, using the post-processing method of the BMA model.