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- W4387234346 abstract "The prevention of water-borne diseases requires the disinfection of water consumed. Disinfection by-products, however, are an increasing concern, and they require advanced knowledge of water treatment plants before their release for human consumption. In this study, multivariate non-linear regression (MNR) and adaptive neuro-fuzzy inference system (ANFIS: Grid partition - GP and Sub-clustering - SC) integrated with particle swarm optimization (PSO) were proposed for the prediction of haloacetic acids (HAAs) in actual distribution systems. PSO-ANFIS-GP and PSO-ANFIS-SC were trained and verified for a total of 64 sets of data with eight parameters (pH, Temperature, UVA254, DOC, Br−; NH4+−N; NO2−−N, residual free chlorine). With MNR, R2 is 0.5184<R2<0.8181 and average absolute error, AAE = 0.172–0.253%). Further, PSO-ANFIS-GP and PSO-ANFIS-SC for HAAs (BCAA, DCAA, TCAA, HAA5, and HAA9) prediction consistently show higher regression coefficients, with lower AAE, (0.8825< R2 < 0.9363 and AAE = 0.1031–0.1499%) and (0.8826< R2 < 0.9322 and AAE = 0.1108–0.1500%) respectively with). With the eight simple parameters, PSO-ANFIS-GP provides an easy way for users to monitor and predict HAAs." @default.
- W4387234346 created "2023-10-02" @default.
- W4387234346 creator A5018975299 @default.
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- W4387234346 creator A5092980181 @default.
- W4387234346 date "2023-12-01" @default.
- W4387234346 modified "2023-10-16" @default.
- W4387234346 title "Performance evaluation of artificial intelligence with particle swarm optimization (PSO) to predict treatment water plant DBPs (haloacetic acids)" @default.
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- W4387234346 doi "https://doi.org/10.1016/j.chemosphere.2023.140238" @default.
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