Radial basis function artificial neural network able to accurately predict disinfection by-product levels in tap water: Taking haloacetic acids as a case study. (June 2020)

• Record Type: Journal Article
• Authors: Lin, Hongjun; Dai, Qunyun; Zheng, Lili; Hong, Huachang; Deng, Wenjing; Wu, Fuyong
• Is Part Of: Chemosphere. Volume 248(2020)
• Publication Date: 2020-06
• Subjects: Disinfection by-products -- Multiple linear/log linear regression -- Radial basis function -- Artificial neural network -- Haloacetic acids; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2020.125999
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: Control of risks caused by disinfection by-products (DBPs) requires pre-knowledge of their levels in drinking water. In this study, a radial basis function (RBF) artificial neural network (ANN) was proposed to predict the concentrations of haloacetic acids (HAAs, one dominant class of DBPs) in actual distribution systems. To train and verify the RBF ANN, a total of 64 samples taken from a typical region (Jinhua region) in China were characterized in terms of water characteristics (dissolved organic carbon (DOC), ultraviolet absorbance at 254 nm (UVA254 ), NO2 − -N level, NH4 + -N level, Br − and pH), temperature and the prevalent HAAs concentrations. Compared with multiple linear/log linear regression (MLR) models, predictions done by RBF ANNs showed rather higher regression coefficients and accuracies, indicating the high capability of RBF ANNs to depict complicated and non-linear relationships between HAAs formation and various factors. Meanwhile, it was found that, predictions of HAAs formation done by RBF ANNs were efficient and allowed to further improve the prediction accuracy. This is the first study to systematically explore feasibility of RBF ANNs in prediction of DBPs. Accurate predictions by RBF ANNs provided great potential application of DBPs monitoring in actual distribution system. Graphical abstract: Image 1 Highlights: RBF ANN well captured the complex relationships between HAA and various factors. RBF ANN prediction showed high accuracy and allowed to further improvement. It is first report to systematically explore feasibility of RBF ANNs in DBPs prediction. The robust RBF ANN for HAA in this study paved a new way to predict DBPs in tap water.