Annual Streamflow Time Series Prediction Using Extreme Learning Machine Based on Gravitational Search Algorithm and Variational Mode Decomposition
Publication: Journal of Hydrologic Engineering
Volume 25, Issue 5
Abstract
Accurate annual runoff prediction plays an important role in modern water resources planning and management. Here, a hybrid model using evolutionary extreme learning machine and variational mode decomposition (VMD) is developed to forecast annual runoff time series. In the proposed method, the VMD method is first used to decompose the original streamflow into a series of disjoint subcomponents; second, each subcomponent is forecasted by constructing an appropriate extreme learning machine model while the gravitational search algorithm is adopted to tune the model parameters; finally, the aggregated output generated by the forecasting results of all the models is treated as the final simulated output. The annual runoff data series of three huge hydropower reservoirs in China are chosen to testify the performance of the proposed forecasting model. The results show that the developed model can outperform several traditional methods with respect to the employed statistical indexes. Thus, the decomposition-ensemble idea is helpful to yield accurate and stable forecasting results, while the proposed forecasting method can provide strong technical support for operators in water resources and power systems.
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Data Availability Statement
Due to the strict security requirements from the departments, some or all data, models, or code generated or used in the study are proprietary or confidential in nature and may only be provided with restrictions (e.g., anonymized data).
Acknowledgments
This paper is supported by the National Key R&D Program of China (2016YFC0402708), National Natural Science Foundation of China (51709119), Natural Science Foundation of Hubei Province (2018CFB573), and the Fundamental Research Funds for the Central Universities (HUST: 2017KFYXJJ193). The authors would like to thank the editors and reviewers for their valuable comments and suggestions.
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Published In
Journal of Hydrologic Engineering
Volume 25 • Issue 5 • May 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Apr 27, 2019
Accepted: Oct 29, 2019
Published online: Feb 20, 2020
Published in print: May 1, 2020
Discussion open until: Jul 20, 2020
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