Open AccessPrediction of water quality based on artificial neural network with grey theory
Author(s) -
W. Zhai,
Xin Zhou,
Jozef De Man,
Qiong Xu,
Qiuping Jiang,
Zhijun Yang,
Lian Jiang,
Zheng-Ming Gao,
Yuan Yao,
Wei Gao
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/295/4/042009
Subject(s) - artificial neural network , regression , artificial intelligence , radial basis function , regression analysis , backpropagation , computer science , mathematics , statistics , pattern recognition (psychology)
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.