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Study of short-term water quality prediction model based on wavelet neural network

文献类型： 外文期刊

作者： Xu, Longqin 1 ; Liu, Shuangyin 1 ;

作者机构： 1.Guangdong Ocean Univ, Coll Informat, Zhanjiang 524025, Guangdong, Peoples R China

2.China Agr Univ, China EU Ctr ICT Agr, Beijing 100083, Peoples R China

3.China Agr Univ, Beijing Engn Res Ctr Agr Internet Things, Beijing 100083, Peoples R China

关键词： Water quality prediction;Wavelet neural network;Wavelet analysis;BP neural network;Intensive pearl breeding

期刊名称：MATHEMATICAL AND COMPUTER MODELLING （ 影响因子：1.366； 五年影响因子：1.602 ）

ISSN：

年卷期：

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收录情况： SCI

摘要： Improved water quality prediction accuracy and reduced computational complexity are vital for ensuring a precise control over the water quality in intensive pearl breeding. This paper combined the wavelet transform with the BP neural network to build the short-term wavelet neural network water quality prediction model. The proposed model was used to predict the water quality of intensive freshwater pearl breeding ponds in Duchang county, Jiangxi province, China. Compared with prediction results achieved by the BP neural network and the Elman neural network, the mean absolute percentage error dropped from 17.464% and 8.438%, respectively, to 3.822%. The results show that the wavelet neural network is superior to the BP neural network and the Elman neural network. Furthermore, the proposed model features a high learning speed, improved predict accuracy, and strong robustness. The model can predict water quality effectively and can meet the management requirements in intensive freshwater pearl breeding.

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