The safety of water delivery and water quality in the South to North Water Transfer Project of China is important to northern China. Water quality data, flow data and data on factors that influence water quality were collected from 25 May to 26 August, 2013. These data were used to forecast water quality and calculate the relative error when using a genetic algorithm optimized general regression neural network (GA-GRNN) model as well as conventional general regression neural network (GRNN) and genetic algorithm optimized back propagation (GA-BP) models. The GA-GRNN method requires few network parameters and has good network stability, a high learning speed and strong approximation ability. The overall forecasted result of GA-GRNN is the best of three models, of which the root mean square error (RMSE) of every index is nearly the least among three models. The results reveal that the GA-GRNN model is efficient for water quality prediction under normal conditions and it can be used to ensure the security of water delivery and water quality in the South to North Water Transfer Project.
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Research Article|
September 29 2014
Prediction of water quality in South to North Water Transfer Project of China based on GA-optimized general regression neural network Available to Purchase
Zhuomin Wang;
1State Key Laboratory of Water Resources & Hydropower Engineering Science, Wuhan University, Luojia Hill, Wuhan 430072, China
E-mail: [email protected]
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Dongguo Shao;
Dongguo Shao
1State Key Laboratory of Water Resources & Hydropower Engineering Science, Wuhan University, Luojia Hill, Wuhan 430072, China
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Haidong Yang;
Haidong Yang
1State Key Laboratory of Water Resources & Hydropower Engineering Science, Wuhan University, Luojia Hill, Wuhan 430072, China
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Shuang Yang
Shuang Yang
1State Key Laboratory of Water Resources & Hydropower Engineering Science, Wuhan University, Luojia Hill, Wuhan 430072, China
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Water Supply (2015) 15 (1): 150–157.
Article history
Received:
May 12 2014
Accepted:
September 15 2014
Citation
Zhuomin Wang, Dongguo Shao, Haidong Yang, Shuang Yang; Prediction of water quality in South to North Water Transfer Project of China based on GA-optimized general regression neural network. Water Supply 1 February 2015; 15 (1): 150–157. doi: https://doi.org/10.2166/ws.2014.099
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