Abstract
Accurate prediction of river discharge is essential for the planning and management of water resources. This study proposes a novel hybrid method named HD-SKA by integrating two decomposition techniques (termed as HD) with support vector regression (SVR), K-nearest neighbor (KNN) and ARIMA models (combined as SKA) respectively. Firstly, the proposed method utilizes local mean decomposition (LMD) to decompose the original river discharge series into sub-series. Next, ensemble empirical mode decomposition (EEMD) is employed to further decompose the LMD-based sub-series into intrinsic mode functions. Further, the EEMD decomposed components are used as inputs in three data-driven models to predict river discharge respectively. The prediction of all components is then aggregated to obtain the results of HD-SVR, HD-KNN and HD-ARIMA models. The final prediction is obtained by taking the average prediction of these models. The proposed method is illustrated using five rivers in Indus Basin System. In five case studies, six models were built to compare the performance of the proposed HD-SKA model. The data analysis results show that the HD-SKA model performs better than all other considered models. The Diebold-Mariano test confirms the superiority of the proposed HD-SKA model over ARIMA, SVR, KNN, EEMD-ARIMA, EEMD-KNN, and EEMD-SVR models.
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Acknowledgements
We would also like to acknowledge the Surface Water Hydrology Project (SWHP) Agency of Water and Power Development Authority (WAPDA), Pakistan for providing the river discharge data for this research work. We are thankful to the Editor and anonymous reviewers for teir comments on earlier versions of the manuscript which improved the paper.
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All the authors jointly worked on the idea. MS collected the data. SC and FI worked on the methodology. MS performed the analysis and prepared the initial draft. SC and FI reviewed and revised the draft.
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Shabbir, M., Chand, S. & Iqbal, F. A Novel Hybrid Method for River Discharge Prediction. Water Resour Manage 36, 253–272 (2022). https://doi.org/10.1007/s11269-021-03026-8
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DOI: https://doi.org/10.1007/s11269-021-03026-8