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Prediction of Chlorophyll-a content using hybrid model of least squares support vector regression and radial basis function neural networks
Wang, Xu1,2; Wang, Guoyin1,2; Zhang, Xuerui2
2016
摘要Eutrophication has become a serious environment problem in many parts of the world and Chlorophyll-a concentration is one of the important parameters for the characterization of water quality, which reflects the degree of eutrophication and algae content in the water body. So establishing a forecasting model to predict the chlorophyll-a concentration in evaluation of eutrophication become more urgent. In this paper, a hybrid model of least squares support vector regression optimized by improved particle swarm optimization and radial basis function neural networks (IPSO-LSSVR-RBFNN) was proposed, which effectively modifying the forecasting accuracy by extracting the useful information in the error term of the traditional methods. A real monthly dataset that collected from a typical reservoir in China during 2010-2012 and two public datasets were used to evaluate the performance of the proposed hybrid model. From the experiment results, we can see that the proposed model of IPSO-LSSVR-RBFNN achieve a higher accuracy rate compared with other models. © 2016 IEEE.
语种英语
DOI10.1109/ICIST.2016.7483440
会议(录)名称6th International Conference on Information Science and Technology, ICIST 2016
页码366-371
通讯作者Wang, Xu
收录类别EI
会议地点Dalian, China
会议日期May 6, 2016 - May 8, 2016