New Artificial Neural Network (Ann) Model for Predicting Disinfection By-Products (Dbps) in Halide-Rich Supplied Water within a Real Water Distribution Network
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Publisher
Elsevier BV
Reference27 articles.
1. Evaluating gas chromatography with a halogen-specific detector for the determination of disinfection by-products in drinking water;A Andersson;Environmental Science and Pollution Research International,2019
2. Models for predicting disinfection byproduct (DBP) formation in drinking waters: A chronological review;S Chowdhury;Science of The Total Environment,2009
3. New methods based on back propagation (BP) and radial basis function (RBF) artificial neural networks (ANNs) for predicting the occurrence of haloketones in tap water;Y Deng;Science of the Total Environment,2021
4. Using regression models to evaluate the formation of trihalomethanes and haloacetonitriles via chlorination of source water with low SUVA values in the Yangtze River Delta region;H Hong;Environmental Geochemistry and Health,2016
5. Radial basis function artificial neural network (RBF ANN) as well as the hybrid method of RBF ANN and grey relational analysis able to well predict trihalomethanes levels in tap water;H Hong;Journal of Hydrology,2020
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