Forecasting reservoir monthly runoff via ensemble empirical mode decomposition and extreme learning machine optimized by an improved gravitational search algorithm |
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| Affiliation: | 1. Key Laboratory of Geographic Information Science (Ministry of Education of China), East China Normal University, Shanghai, China;2. School of Geographical Sciences, East China Normal University, Shanghai, China;3. Software Engineering Institute, East China Normal University, Shanghai, China;4. Computer Center, East China Normal University, China;5. Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China;6. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China;7. Beijing Institute of Big Data Research, Beijing, China;1. Department of Civil Engineering, Graduate University of Advanced Technology-Kerman, P.O. Box 76315-116, Kerman, Iran;2. Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju 36040, South Korea;3. Department of Civil Engineering, Monash University, 23 College Walk, Clayton, VIC 3800, Australia;1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;2. Faculty of Civil Engineering & Architecture, University of Nis, Aleksandra Medvedeva 14, Nish 18000, Serbia;3. Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran;4. School of Technology, Ilia State University, Tbilisi, Georgia;1. Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, PR China;2. Department of Biological and Agricultural Engineering, and Zachry Department of Civil Engineering, Texas A & M University, College Station, TX 77843, USA;3. Nanjing Hydraulic Research Institute, Nanjing, PR China |
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| Abstract: | Monthly streamflow prediction plays a significant role in reservoir operation and water resource management. Hence, this research tries to develop a hybrid model for accurate monthly streamflow prediction, where the ensemble empirical mode decomposition (EEMD) is firstly used to decompose the original streamflow data into a finite amount of intrinsic mode functions (IMFs) and a residue; and then the extreme learning machine (ELM) is employed to forecast each IMFs and the residue, while an improved gravitational search algorithm (IGSA) based on elitist-guide evolution strategies, selection operator and mutation operator is used to select the parameters of all the ELM models; finally, the summarized predicated results for all the subcomponents are treated as the final forecasting result. The hybrid method is applied to forecast the monthly runoff of Three Gorges in China, while four quantitative indexes are used to test the performances of the developed forecasting models. The results show that EEMD can effectively separate the internal characteristics of the original monthly runoff, and the hybrid model is able to make an obvious improvement over other models in hydrological time series prediction. |
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| Keywords: | Monthly streamflow prediction Ensemble empirical mode decomposition (EEMD) Extreme learning machine (ELM) Improved gravitational search algorithm (IGSA) Mutation and selection operators |
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