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Article

Prediction and Interpretation of Water Quality Recovery after a Disturbance in a Water Treatment System Using Artificial Intelligence

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Department of Civil and Environmental Engineering, Hanbat National University, 125, Dongseo-daero, Yuseong-gu, Daejeon 34158, Korea
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GNC Environmental Solution, 201, 24, Umuk-gil 52beon-gil, Chuncheon-si 24368, Korea
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Waterworks Research Institute, Waterworks Headquarters Incheon Metropolitan City, 332, Bupyeong-daero, Bupyeong-gu, Incheon 22101, Korea
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Department of Environmental Research, Korea Institute of Civil Engineering and Building Technology, 283, Goyang-daero, Ilsanseo-gu, Goyang-si 10223, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Gwo-Fong Lin
Water 2022, 14(15), 2423; https://doi.org/10.3390/w14152423
Received: 7 July 2022 / Revised: 2 August 2022 / Accepted: 3 August 2022 / Published: 5 August 2022
(This article belongs to the Section Water Quality and Contamination)
In this study, an ensemble machine learning model was developed to predict the recovery rate of water quality in a water treatment plant after a disturbance. XGBoost, one of the most popular ensemble machine learning models, was used as the main framework of the model. Water quality and operational data observed in a pilot plant were used to train and test the model. Disturbance was determined when the observed turbidity was higher than the given turbidity criteria. Therefore, the recovery rate of water quality at a time t was defined during the falling limb of the turbidity recovery period. It was considered as a relative ratio of the differences between the peak and observed turbidities at time t to the difference between the peak turbidity and turbidity criteria. The root mean square error–observation standard deviation ratio of the XGBoost model improved from 0.730 to 0.373 by pretreatment, removing the observation for the rising limb of the disturbance from the training data. Moreover, Shapley value analysis, a novel explainable artificial intelligence method, was used to provide a reasonable interpretation of the model’s performance. View Full-Text
Keywords: ensemble model; explainable artificial intelligence; machine learning; water treatment system; XGBoost ensemble model; explainable artificial intelligence; machine learning; water treatment system; XGBoost
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MDPI and ACS Style

Park, J.; Ahn, J.; Kim, J.; Yoon, Y.; Park, J. Prediction and Interpretation of Water Quality Recovery after a Disturbance in a Water Treatment System Using Artificial Intelligence. Water 2022, 14, 2423. https://doi.org/10.3390/w14152423

AMA Style

Park J, Ahn J, Kim J, Yoon Y, Park J. Prediction and Interpretation of Water Quality Recovery after a Disturbance in a Water Treatment System Using Artificial Intelligence. Water. 2022; 14(15):2423. https://doi.org/10.3390/w14152423

Chicago/Turabian Style

Park, Jungsu, Juahn Ahn, Junhyun Kim, Younghan Yoon, and Jaehyeoung Park. 2022. "Prediction and Interpretation of Water Quality Recovery after a Disturbance in a Water Treatment System Using Artificial Intelligence" Water 14, no. 15: 2423. https://doi.org/10.3390/w14152423

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