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Water quality prediction based on improved wavelet transformation and support vector machine
Liu, Wen1,2; Wang, Guo Yin1,2; Fu, Jian Yu2; Zou, Xuan1,2
2013
摘要In the process of monitoring water quality, as the transient variable data lead to unsound prediction models and the traditional parameter optimization method based on signal factor experiments is not only time-consuming but also can not ensure the most optimal parameters. We propose to combine wavelet transformation with data translation to reduce the influence of transient variations on prediction models, and use genetic algorithm (GA) to optimize the parameters of support vector machine (SVM). The new prediction model is applied to predict water quality time series, which is compared with the traditional modeling methods based on SVM and BP neural network. The results show that the new model is superior to traditional modeling methods. © (2013) Trans Tech Publications, Switzerland.
语种英语
DOI10.4028/www.scientific.net/AMR.726-731.3547
会议(录)名称2013 2nd International Conference on Energy and Environmental Protection, ICEEP 2013
页码3547-3553
收录类别EI
会议地点Guilin, China
会议日期April 19, 2013 - April 21, 2013