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《Journal of Hydroelectric Engineering》 2011-03
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Short-term runoff prediction based on adaptive regulation ant colony system and radial basis function neural network hybrid algorithm

BAI Jizhong1,2,SHI Biao1,FENG Minquan1,ZHOU Likun1,3,LI Xiaolong1(1.Institute of Water Resources and Hydro-electric Engineering,Xi'an University of Technology,Xi'an 710048; 2.Shan xi Water Conservancy Technical Institute,Yun cheng 044004,China; 3.Engineering College of CAPF,Xi'an 710086)  
Short-term runoff prediction is a major responsibility of water conservancy departments.To improve the accuracy of reservoir short-term runoff forecast,an adaptive regulation ant colony system(ARACS) algorithm is proposed.A forecast model was developed by using this new algorithm in combination with a radial basis function(RBF) neural network that was trained by using ARACS and an ARACS-RBF hybrid algorithm was obtained.This model can automatically determine the parameters of the neural network from the data sample.Predictions of the short-term runoff of practical reservoirs were made using the model with a comprehensive consideration of impacting factors such as meteorology,weather,rainfall and seasonal change.The result shows a faster convergence and a better forecast accuracy of the hybrid method than those of the traditional ant colony system algorithm-RBF neural network or RBF neural network,and also a significant improvement on the generalization capacity of RBF neural network.An average percentage error of no more than 3% was achieved.Thus,the hybrid algorithm enhances the efficiency of short-term load forecast of the reservoir and river.
【Fund】: 国家火炬计划基金(07C26213711606);; 山西省水利厅科技计划基金(2009WK110)
【CateGory Index】: TP183
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