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.
Prediction of water quality in South to North Water Transfer Project of China based on GA-optimized general regression neural network
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 Science and Technology: Water Supply 1 February 2015; 15 (1): 150–157. doi: https://doi.org/10.2166/ws.2014.099
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