Improving Prediction Accuracy of Hydrologic Time Series by Least-Squares Support Vector Machine Using Decomposition Reconstruction and Swarm Intelligence
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Volume 26, Issue 9
Abstract
Accurate hydrologic forecasting plays a significant role in water resource planning and management. To improve the prediction accuracy, this study develops a hybrid hydrological forecasting method based on signal decomposition reconstruction and swarm intelligence. Firstly, the ensemble empirical mode decomposition is utilized to divide the nonlinear runoff data series into several simple subsignals. Secondly, the least-squares support vector machine using the gravitational search algorithm is used to recognize the relationship between previous inputs and the target output in each subsignal. Next, the forecasting result is obtained by summarizing the total outputs of all the models. Four famous indexes are used to evaluate the performances of various forecasting models in monthly runoff of two hydrological stations in China. The applications in different scenarios show that the hybrid method obtains better results than several control models. For the runoff at Cuntan Station, the hybrid method makes 58.9% and 52.4% improvements in the root-mean squared error value compared with the artificial neural network and support vector machine at the training phase. Thus, a practical data-driven tool is developed to predict hydrological time series.
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Data Availability Statement
Due to the strict security requirements from the departments, all the 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 Fundamental Research Funds for the Central Universities (B210201046), the National Natural Science Foundation of China (52009012), Natural Science Foundation of Hubei Province (2020CFB340), and National Natural Science Foundation of China (U1865202 and 51709119). The authors would like to thank editors and reviewers for their valuable comments and suggestions.
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Journal of Hydrologic Engineering
Volume 26 • Issue 9 • September 2021
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© 2021 American Society of Civil Engineers.
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Received: Sep 10, 2020
Accepted: May 3, 2021
Published online: Jul 7, 2021
Published in print: Sep 1, 2021
Discussion open until: Dec 7, 2021
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