Integrating water quality and operation into prediction of water production in drinking water treatment plants by genetic algorithm enhanced artificial neural network

Water Research
Yanyang ZhangBingcai Pan

Abstract

Stringent regulations and deteriorating source water quality could greatly influence the water production capacity of drinking water treatment plants (DWTPs). Using models to predict the performance of DWTPs under stress provides valuable information for decision making and future planning. A hybrid statistic model named HANN was established by combining artificial neural network (ANN) with genetic algorithm (GA) aiming at forecasting the overall performance of DWTPs nationwide in China. Monthly data from 45 DWTPs across China was employed. Water quality parameters like temperature and chemical oxygen demand (COD) and operational parameters like electricity consumption and chemical consumption were selected as input variables, while drinking water production was employed as the output. Both preliminary data analysis and principal component analysis (PCA) suggested a clear non-linear relationship between the input and output variables. The structure of the HANN model was optimized by employing the lowest mean squared error (MSE) as the indicator. The resultant HANN model performed well when simulating the training datasets. Its predictive accuracy for the independent test datasets was enhanced when feeding more training datasets...Continue Reading

Citations

Feb 23, 2020·Environmental Science and Pollution Research International·Mohammad EhteramZaher Mundher Yaseen
Aug 22, 2020·Environmental Science and Pollution Research International·Mohamed K Abdel-FattahAhmed I Abdo
Feb 20, 2020·International Journal of Environmental Research and Public Health·Patricia Jimeno-SáezJulio Pérez-Sánchez
Sep 1, 2020·Journal of Environmental Management·Kent McClymontMaryam Imani
Nov 13, 2020·Environmental Science and Pollution Research International·Muhammad Izhar ShahTaher Abunama
Oct 6, 2021·Environmental Science and Pollution Research International·Waidah IsmailMohd Zamani Zulkifli

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