Applicability of a Three-Stage Hybrid Model by Employing a Two-Stage Signal Decomposition Approach and a Deep Learning Methodology for Runoff Forecasting at Swat River Catchment, Pakistan
Muhammad Sibtain 1  
,   Xianshan Li 1  
,   Muhammad Imran Azam 2  
,   Hassan Bashir 3  
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Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University
College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 44302, P. R. China
College of Environmental Science and Engineering, Hunan University, Changsha 410082, P. R. China
Muhammad Sibtain   

Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, 44302, Yichang, China
Submission date: 2020-01-22
Final revision date: 2020-04-17
Acceptance date: 2020-04-18
Online publication date: 2020-08-22
Publication date: 2020-10-05
Pol. J. Environ. Stud. 2021;30(1):369–384
The optimal management of hydropower resources is highly dependent on accurate and reliable hydrological runoff forecasting. The development of a suitable runoff-forecasting model is a challenging task due to the complex and nonlinear nature of runoff. To meet the challenge, this study proposed a three-stage novel hybrid model namely IVG (ICEEMDAN-VMD-GRU), by coupling gated recurrent unit (GRU) with a two-stage signal decomposition methodology, combining improved complete ensemble empirical decomposition with additive noise (ICEEMDAN) and variational mode decomposition (VMD), to forecast the monthly runoff of SWAT river, Pakistan. ICEEMDAN decomposed the runoff time series into subcomponents, and VMD performed further decomposition of the high-frequency component obtained by ICEEMDAN decomposition. Afterward, the GRU network was employed to the decomposed subcomponents for forecasting purposes. The performance of the IVG model was compared with other hybrid models including, ICEEMDAN-VMD-SVM (support vector machine), ICEEMDANGRU, VMD-GRU, ICEEMDAN-SVM, VMD-SVM; and standalone models including GRU and SVM by utilizing statistical indices. Experimental results proved that the IVG model outperformed other models in terms of accuracy and error reduction, which indicates the feasibility of the IVG model to analyze the nonlinear features of runoff time series and for runoff forecasting with applicability for future planning and management of water resources.