CLC number: TU991
On-line Access:
Received: 2006-12-26
Revision Accepted: 2007-04-09
Crosschecked: 0000-00-00
Cited: 21
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ZHAO Ying, NAN Jun, CUI Fu-yi, GUO Liang. Water quality forecast through application of BP neural network at Yuqiao reservoir[J]. Journal of Zhejiang University Science A, 2007, 8(9): 1482-1487.
@article{title="Water quality forecast through application of BP neural network at Yuqiao reservoir",
author="ZHAO Ying, NAN Jun, CUI Fu-yi, GUO Liang",
journal="Journal of Zhejiang University Science A",
volume="8",
number="9",
pages="1482-1487",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A1482"
}
%0 Journal Article
%T Water quality forecast through application of BP neural network at Yuqiao reservoir
%A ZHAO Ying
%A NAN Jun
%A CUI Fu-yi
%A GUO Liang
%J Journal of Zhejiang University SCIENCE A
%V 8
%N 9
%P 1482-1487
%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A1482
TY - JOUR
T1 - Water quality forecast through application of BP neural network at Yuqiao reservoir
A1 - ZHAO Ying
A1 - NAN Jun
A1 - CUI Fu-yi
A1 - GUO Liang
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 9
SP - 1482
EP - 1487
%@ 1673-565X
Y1 - 2007
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2007.A1482
Abstract: This paper deals with the study of a water quality forecast model through application of BP neural network technique and GUI (Graphical User Interfaces) function of MATLAB at Yuqiao reservoir in Tianjin. To overcome the shortcomings of traditional BP algorithm as being slow to converge and easy to reach extreme minimum value, the model adopts LM (Levenberg-Marquardt) algorithm to achieve a higher speed and a lower error rate. When factors affecting the study object are identified, the reservoir’s 2005 measured values are used as sample data to test the model. The number of neurons and the type of transfer functions in the hidden layer of the neural network are changed from time to time to achieve the best forecast results. Through simulation testing the model shows high efficiency in forecasting the water quality of the reservoir.
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