Abstract
Precise and reliable hydrological runoff prediction plays a significant role in the optimal management of hydropower resources. Nevertheless, the hydrological runoff practically possesses a nonlinear dynamics, and constructing appropriate runoff prediction models to deal with the nonlinearity is a challenging task. To overcome this difficulty, this paper proposes a three-stage novel hybrid model, namely, CVS (CEEMDAN-VMD-SVM), by coupling the support vector machine (SVM) with a two-stage signal decomposition methodology, combining complete ensemble empirical decomposition with additive noise (CEEMDAN) and variational mode decomposition (VMD), to obtain inclusive information of the runoff time series. Hydrological runoff data of the Swat River, Pakistan, from 1961 to 2015 were taken for prediction. CEEMDAN decomposes the runoff time series into subcomponents, and VMD performs further decomposition of the high-frequency component obtained after CEEMDAN decomposition to improve the prediction activity. Afterward, the SVM algorithm was applied to the decomposed subcomponents for the prediction purpose. Finally, four statistical indices are utilized to measure the performance of the CVS model compared with other hybrid models including CEEMDAN-VMD-MLP (multilayer perceptron), CEEMDAN-SVM, VMD-SVM, CEEMDAN-MLP, VMD-MLP, SVM, and MLP. The CVS model performs better during the training period by reducing RMSE by 71.28% and 40.06% compared with MLP and CEEDMAD-VMD-SVM models, respectively. However, during the testing period, the error reductions include RMSE by 68.37% and 35.33% compared with MLP and CEEDMAD-VMD-SVM models, respectively. The results highlight that the CVS model outperforms other models in terms of accuracy and error reduction. The research also highlights the superiority of other hybrid models over standalone in predicting the hydrological runoff. Therefore, the proposed hybrid model is applicable for the nonlinear features of runoff time series with feasibility for future planning and management of water resources.
Highlights
Managing water resources is crucial from several aspects such as the development of future water bodies, efficient exploitation of hydropower for power generation and irrigation purposes, to prevent disputes, and for the protection of existing water bodies from overexploitation and pollution [1, 2]
The hybrid model based on two-layer decomposition methodology is employed to overcome the limitations of the single-layer decomposition technique [43]. erefore, this study proposes a three-stage hybrid model based on complete ensemble empirical decomposition with additive noise (CEEMDAN), variational mode decomposition (VMD), and support vector machine (SVM) and its applicability to the runoff time series. e first decomposition stage employs the CEEMDAN technique and decomposes the runoff series into random, periodic, and trend components intending to improve the prediction of nonlinear and nonstationary monthly runoff series. e VMD is proposed as an additional decomposition technique to diminish stochastic behaviors, noise, and trends of the data
Hydrological runoff exhibits nonstationary and nonlinear characteristics [79, 80]. ese properties of runoff result in the undesirable performance of many prediction models, along with poor generalization due to the requirements of many pseudo-variations, which affects the accurate knowledge of data variations [81]. erefore, this paper proposes a three-stage hybrid model by coupling the Machine learning (ML) method with signal decomposition techniques for a reliable and accurate runoff prediction
Summary
Managing water resources is crucial from several aspects such as the development of future water bodies, efficient exploitation of hydropower for power generation and irrigation purposes, to prevent disputes, and for the protection of existing water bodies from overexploitation and pollution [1, 2]. Complete ensemble empirical mode decomposition with additive noise (CEEMDAN) is an advanced technique that overcomes the issues faced by EMD and EEMD like mode mixing and computational complexity, respectively. Ese hybrid models consisted of a single-layer decomposition technique that can enhance the predictive performance of nonlinear time series to some extent but unable to completely predict the nonlinearity and nonstationarity of the original signals. Erefore, this study proposes a three-stage hybrid model based on CEEMDAN, VMD, and SVM and its applicability to the runoff time series. E first decomposition stage employs the CEEMDAN technique and decomposes the runoff series into random, periodic, and trend components intending to improve the prediction of nonlinear and nonstationary monthly runoff series. E rest of the paper is arranged as follows. e modeling techniques and the proposed approach are described in Section 2. e results and discussion are presented in Section 3, while Section 4 concludes the research work. e research will be useful for prediction and planning purposes and will provide new directions in the field of hydrology
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