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Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis

Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
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Int. J. Environ. Res. Public Health 2021, 18(3), 1023; https://doi.org/10.3390/ijerph18031023
Received: 4 January 2021 / Revised: 21 January 2021 / Accepted: 21 January 2021 / Published: 24 January 2021
(This article belongs to the Special Issue Diffuse Water Pollution Modeling, Monitoring and Mitigation 2020)
To minimize the damage from contaminant accidents in rivers, early identification of the contaminant source is crucial. Thus, in this study, a framework combining Machine Learning (ML) and the Transient Storage zone Model (TSM) was developed to predict the spill location and mass of a contaminant source. The TSM model was employed to simulate non-Fickian Breakthrough Curves (BTCs), which entails relevant information of the contaminant source. Then, the ML models were used to identify the BTC features, characterized by 21 variables, to predict the spill location and mass. The proposed framework was applied to the Gam Creek, South Korea, in which two tracer tests were conducted. In this study, six ML methods were applied for the prediction of spill location and mass, while the most relevant BTC features were selected by Recursive Feature Elimination Cross-Validation (RFECV). Model applications to field data showed that the ensemble Decision tree models, Random Forest (RF) and Xgboost (XGB), were the most efficient and feasible in predicting the contaminant source. View Full-Text
Keywords: contaminant source identification; transient storage zone model; breakthrough curve analysis; ensemble decision tree model; recursive feature elimination cross-validation; tracer test contaminant source identification; transient storage zone model; breakthrough curve analysis; ensemble decision tree model; recursive feature elimination cross-validation; tracer test
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MDPI and ACS Style

Kwon, S.; Noh, H.; Seo, I.W.; Jung, S.H.; Baek, D. Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis. Int. J. Environ. Res. Public Health 2021, 18, 1023. https://doi.org/10.3390/ijerph18031023

AMA Style

Kwon S, Noh H, Seo IW, Jung SH, Baek D. Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis. International Journal of Environmental Research and Public Health. 2021; 18(3):1023. https://doi.org/10.3390/ijerph18031023

Chicago/Turabian Style

Kwon, Siyoon; Noh, Hyoseob; Seo, Il W.; Jung, Sung H.; Baek, Donghae. 2021. "Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis" Int. J. Environ. Res. Public Health 18, no. 3: 1023. https://doi.org/10.3390/ijerph18031023

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