Machine learning modeling of fluorescence spectral data for prediction of trace organic contaminant removal during UV/H2O2 treatment of wastewater

• Published in: Water research (Oxford) Vol. 255; p. 121484
• Main Authors: Yang, Yi, Shan, Chao, Pan, Bingcai
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.05.2024
• Subjects: Advanced oxidation; data collection; Excitation-emission matrix; fluorescence; Machine learning; Micropollutant removal; Multi-target regression random forest model; oxidation; photolysis; prediction; regression analysis; spectral analysis; wastewater treatment; water; Micropollutant removal; Multi-target regression random forest model; Advanced oxidation; Excitation-emission matrix; Machine learning

•Machine learning of EEM is effective in predicting TrOCs removal during AOP.•Machine learning model outperforms traditional linear regression model in accuracy.•Simultaneous prediction of multiple TrOCs removal was achieved using MORF model.•Fluorescence spectral regions of high importance were ide...