Monthly Mean Streamflow Prediction Based on Bat Algorithm-Support Vector Machine
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Volume 21, Issue 2
Abstract
Accurate and reliable prediction of runoff generation is necessary for flood control scheduling, water supply planning, and hydropower generation. Support vector machine (SVM), which is at the forefront of current research of regression and classification, was used in this paper to conduct monthly mean streamflow prediction. A novel heuristic optimization named bat algorithm (BA) was introduced to determine the parameters of SVM [penalty parameter () and kernel parameter ()], in which the initial fitness was supposed to be equal to the initial loudness for all bats. In order to evaluate the effectiveness of the proposed approach, monthly mean streamflow from 1952 to 2011 of Yichang station in the middle reaches of the Yangtze River were trained and tested. In the meantime, the given data set was also modeled using artificial neural networks (ANN) and cross validation–based SVM. The comparison results indicate that the proposed model (bat algorithm–based SVM) is more accurate compared with both ANN and cross validation–based SVM. However, two main shortages exist, i.e., time-consuming and relatively low accuracy in the break points of continued dry (wet) years. To relieve these shortages, local optimization algorithms [e.g., differential evolution (DE) algorithm, immune algorithm (IA), and genetic algorithm (GA)] were suggested to be combined with the bat algorithm to produce the initial population. Modifications of the stochastic term of the local search were also useful.
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Acknowledgments
This work was supported by National Natural Science Foundation of China Grant (41171022). We thank professor Yang Xinshe in Cambridge University for providing the related technical documents about bat algorithm. We are also grateful to Tingbing Xu and Huiquan Tan for their enlightening discussions.
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Journal of Hydrologic Engineering
Volume 21 • Issue 2 • February 2016
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© 2015 American Society of Civil Engineers.
History
Received: Jun 19, 2014
Accepted: May 27, 2015
Published online: Jul 20, 2015
Discussion open until: Dec 20, 2015
Published in print: Feb 1, 2016
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