# Investigation of the EWT-PSO-SVM Model for Runoff Forecasting in the Karst Area
## 5 Conclusions  
Due to the complexity of karst basin structure, runoff forecasting has always faced great challenges. According to the \"decomposition-prediction-reconstruction\" process, the following research steps were taken for the hybrid model EWT-PSO-SVM to forecast runoff. First, EWT is employed to split the runoff series into subseries for the purpose of reducing series nonlinearity and nonstationarity. Second, the parameters of SVM are selected using PSO, and then the subseries are substituted into the optimized SVM model for prediction. Finally, the predicted values of every subseries are reconstructed for the final runoff forecasting results. The runoff data from the Chengbi River K. Kast Basin was substituted into the different models for forecasting to test the superiority of the developed model. The comprehensive evaluation index of the developed model reached 0.68 and the maximum error was reduced by 43.12% compared with the SVM model. Meanwhile, the monthly structure data was fed into the different models for prediction to further verify the stability of the developed model. The composite evaluation index of the developed model under the monthly structure reached 0.67. The results show that the EWT-PSO-SVM model exhibits better performance than the single SVM model under different data structures, which indicates that data decomposition and parameter optimization strategies can effectively enhance the precision of a single SVM model. In this way, the developed EWT-PSO-SVM model makes a prospective method for predicting nonlinear and nonstationary runoff series in karst basins.  
Although the developed model has shown good overall forecasting performance, there are still some limitations. The developed model has some prediction errors in predicting low values in the series. Therefore, future research directions could consider separating high and low values in the runoff series for prediction to obtain higher prediction accuracy. Meanwhile, it is valuable to further investigate the hybrid performance of EWT with other artificial intelligence or machine learning methods to enhance the accuracy of runoff forecasting.  
Conceptualization, C.M., Z.Y., X.L. and Y.D.; methodology, Z.Y., X.L. and Y.D.; software, Y.D.; validation, C.M., Z.Y. and S.L.; formal analysis C.M., Z.Y. and S.L.; investigation, C.M., Z.Y. and S.L.; resources, C.M.; data curation, K.H.; writing--original draft preparation, Z.Y. and Y.D.; writing--review and editing, X.L., S.L. and K.H.; supervision, X.L.; project administration, C.M. and R.M.; funding acquisition, C.M., S.L. and X.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 52269002, 51969004), the science and technology award incubation project of Guangxi University (Grant No. 2022 BZJL023), the Interdisciplinary Scientific Research Foundation of Guangxi University (Grant No. 2022 JC0028), the Science and Technology Award Incubation Project of Guangxi University (Grant No. 2022 BZJL023), and the Guangxi Water Resource Technology Promotion Foundation (SK2021-3-23).  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** Some data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.