Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 38, Issue 10, Pages 13073-13081Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2011.04.114
Keywords
Support vector machine; Streamflow forecast; Adaptive insensitive factor; Wavelet; Chaos and phase-space reconstruction theory; Artificial neural network
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Funding
- National Basic Research Program of China (973 Program) [2007CB714107]
- National Science and Technology Planning Project [2008BAB29B08]
- Special Research Foundation for the Public Welfare Industry of the Ministry of Science and Technology
- Ministry of Water Resources [200701008]
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To improve the performance of the support vector machine (SVM) model in predicting monthly streamflow, an improved SVM model with adaptive insensitive factor is proposed in this paper. Meanwhile. considering the influence of noise and the disadvantages of traditional noise eliminating technologies, here the wavelet denoise method is applied to reduce or eliminate the noise in runoff time series. Furthermore, in order to avoid the subjective arbitrariness of artificial judgment, the phase-space reconstruction theory is introduced to determine the structure of the streamflow prediction model. The feasibility of the proposed model is demonstrated through a case study, and the results are compared with the results of artificial neural network (ANN) model and conventional SVM model. The results verify that the improved SVM model can process a complex hydrological data series better, and is of better generalization ability and higher prediction accuracy. (C) 2011 Elsevier Ltd. All rights reserved.
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