Additive Model for Monthly Reservoir Inflow Forecast
Publication: Journal of Hydrologic Engineering
Volume 20, Issue 7
Abstract
Reservoir inflow forecasting plays an essential role in reservoir operation and management. Considering the characteristics of monthly inflow (trend, seasonality, and randomness throughout the hydrologic year), an additive model is proposed to forecast monthly reservoir inflow. Because different features are represented by different frequency bands of the time series, historical time series of the monthly inflow are decomposed by ensemble empirical mode decomposition into several intrinsic mode functions and a residue. According to frequency signatures analyzed by Fourier spectral representation, all intrinsic mode functions and residue are grouped into three terms: trend term, periodic term, and stochastic term. To accommodate the different characteristics of the three terms, an autoregressive model, a least-squares support vector machine, and an adaptive neuro-fuzzy inference system model are adopted for the three subforecasts, respectively. The additive model is subsequently used to integrate the three subforecasts representing different characteristics to achieve the final forecasting results. The proposed method is applied to the Three Gorges Reservoir in China, using data from January 2000 to December 2012. For comparison, the three terms’ models and two peer models—back-propagation neural network and autoregressive integrated moving average—are adopted for monthly inflow forecasting. Among all six approaches, the present additive model exhibits the best forecasting performance of mean absolute percentage error, 11.36%, normalized root-mean-square error, 0.15, and correlation coefficient 0.97.
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Acknowledgments
This work is supported in part by the National Natural Science Foundation of China (51375517, 71271226), the Natural Science Foundation of CQ CSTC (2012JJJQ70001), the Project of Chongqing Innovation Team in University (KJTD201313), the National Key Technology Research and Development Program of China (2012BAH32F01, 03), and the 111 Project (No. B13041). The authors also thank the two reviewers for their valuable suggestions and comments, which helped improve this paper.
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Journal of Hydrologic Engineering
Volume 20 • Issue 7 • July 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Feb 13, 2014
Accepted: Sep 11, 2014
Published online: Oct 15, 2014
Discussion open until: Mar 15, 2015
Published in print: Jul 1, 2015
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