Prediction of water quality based on artificial neural network with grey theory

2019 5th International Conference on Energy Materials and Environment Engineering

能源学;生态环境科学

Zhai, W.^1 ; Zhou, X.^2^3 ; Man, J.^4 ; Xu, Q.^1 ; Jiang, Q.^1 ; Yang, Z.^1 ; Jiang, L.^1 ; Gao, Z.^1 ; Yuan, Y.^1 ; Gao, W.^1

School of Safety Engineering, Ningbo University of Technology, Ningbo, Zhejiang

315211, China^1

School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, Hubei

430070, China^2

School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan, Hubei

430070, China^3

Ningbo Liwah Pharmaceutical Co., Ltd., Zhenhai Branch, Ningbo, Zhejiang

315204, China^4

关键词: Back propagation neural networks;    Generalized regression neural networks;    Grey theory;    P-values;    pH value;    Radial basis function neural networks;    Relative errors;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/295/4/042009/pdf
DOI  :  10.1088/1755-1315/295/4/042009

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

In this paper, the grey theory, three type of artificial neural network (back-propagation neural network, radial basis function neural network, and generalized regression neural network) and their combination were used to predict the pH values in the evaluation of water quality. Based on the measured data from the Xielugang in Jiaxin with the post-hoc analysis for the c and p values of the prediction, the results showed that the prediction by using the generalized regression neural network has the averaged relative error 0.61%, and c 0.7.