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

被引：6

作者：

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
]}

机构：

[1] Ningbo Univ Technol, Sch Safety Engn, Ningbo 315211, Zhejiang, Peoples R China

[2] Wuhan Univ Technol, Sch Safety Sci & Emergency Management, Wuhan 430070, Hubei, Peoples R China

[3] Wuhan Univ Technol, Sch Resources & Environm Engn, Wuhan 430070, Hubei, Peoples R China

[4] Ningbo Liwah Pharmaceut Co Ltd, Zhenhai Branch, Ningbo 315204, Zhejiang, Peoples R China

来源：

2019 5TH INTERNATIONAL CONFERENCE ON ENERGY MATERIALS AND ENVIRONMENT ENGINEERING | 2019年 / 295卷

关键词：

D O I：

10.1088/1755-1315/295/4/042009

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

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 <0.65, p>0.7.

引用

页数：4

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