Research of predictive model of drinking water quality based on wavelet neural network / 重庆医科大学学报

ABSTRACT

Objective:To provide the methodology reference for the drinking water quality prediction.Methods:A predictive model of drinking water quality was established by wavelet neural network.The monthly average concentration of potassium permanganate in Chongqing,one drinking water quality parameter,was predicted by the model,and the predictive results were compared with BP neural network.Results:RMSE and MAPE were applied to evaluate the predictive results.The research indicated that the precision of WNN model was superior to that of BP neural network model.Conclusion:The WNN model has better precision for drinking water quality prediction.