Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea.
Int J Environ Res Public Health. 2021 Jan 24;18(3):1023. doi: 10.3390/ijerph18031023.
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.
为了将河流中污染物事故的损害降到最低，尽早识别污染源至关重要。因此，在本研究中，开发了一个结合机器学习（ML）和瞬态储存区模型（TSM）的框架，以预测污染物源的泄漏位置和质量。该 TSM 模型用于模拟非菲克突破曲线（BTC），其中包含污染物源的相关信息。然后，使用 ML 模型来识别 BTC 特征，由 21 个变量来预测泄漏位置和质量。该框架应用于韩国甘溪（Gam Creek），在该溪进行了两次示踪剂试验。在本研究中，应用了六种 ML 方法来预测泄漏位置和质量，同时通过递归特征消除交叉验证（RFECV）选择了最相关的 BTC 特征。模型在现场数据中的应用表明，集成决策树模型、随机森林（RF）和 Xgboost（XGB）在预测污染源方面是最有效和可行的。
