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《Journal of Water Resources and Water Engineering》 2013-06
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EEMD-LSSVM model of combined forecast of annual runoff based on parameter optimization

ZHANG Guoyong;WU Yonggang;YANG Linming;WANG Pengfei;College of Hydropower and Information Engineering,Huazhong University of Science and Technology;  
Runoff forecast is the base of water resources management and its accuracy has a critical influence on the optimization scheduling of water resources. Aimed at the inner periodic feature of runoff time series,a hybrid annual runoff forecast method based on ensemble empirical mode decomposition( EEMD) and least squares support vector machines( LSSVM) was proposed in this paper. To solve the problem of the uncertain parameters of LSSVM,a particle swarm optimization algorithm based on dynamicapproximation research was proposed to optimize the parameter in LSSVM. Based on the principle of decomposition and ensemble,the runoff series is decomposed into lots of periodic components first. And then,the LSSVM model is used to forecast and reconstruct these components of intrinsic mode function. In comparison to the results of three methods in the annual runoff forecast of Jiangya station in Lishui River,witch shows that the proposed runoff forecast model is reliable.
【Fund】: 湖北省自然科学基金重点项目(2011CDA032)
【CateGory Index】: P334.92
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