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首页> 外文期刊>Journal of hydro-environment research >Prediction of fecal coliform using logistic regression and tree-based classification models in the North Han River, South Korea
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Prediction of fecal coliform using logistic regression and tree-based classification models in the North Han River, South Korea

机译：使用Logistic回归和基于树的分类模型在韩国北汉江中预测粪便大肠菌群

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摘要

In this study, data-based classification models were developed for real-time prediction of the exceedance of the safety level on fecal coliform in Daesung-ri site of North Han River. The prediction models were developed using the logistic regression model (LRM) and the tree-based models such as classification and regression model (CART), bagging model (BGM), and random forest model (RFM). For model development, rainfall, water quality, and dam discharge data from 2010 to 2015 were collected from the study site. Clustering methods were applied to reduce the sampling bias of training and test datasets and to improve the prediction accuracy. The developed four models were compared with each other in terms of prediction accuracy and applicability. The test results of developed models showed that the total correct classification rate of the four models ranged from 83.7% to 93.0%. Each classification model showed its own strengths; LRM offered flexibility by tuning cutoff values, while RFM showed the highest accuracy among the four models. The hydro-ecological process on fecal coliform could be explained by analyzing important variables in the prediction models and identifying the impacting factors through the field monitoring. The important factors both in the models and field monitoring were revealed as the rainfall-related variables, dam discharge and total phosphorus, which imply that the fecal pollution in North Han River came mainly from the rainfall events and runoff including nutrients from farmland and livestock farming in the upstream basin of Guwoon Creek and Chungpyung Dam.
机译：在这项研究中，开发了基于数据的分类模型，用于实时预测北汉江大成里站点粪便大肠菌群安全水平的超标情况。预测模型是使用逻辑回归模型（LRM）和基于树的模型（例如分类和回归模型（CART），装袋模型（BGM）和随机森林模型（RFM））开发的。为了进行模型开发，从研究地点收集了2010年至2015年的降雨，水质和大坝流量数据。应用聚类方法来减少训练和测试数据集的采样偏差并提高预测精度。将开发的四个模型在预测准确性和适用性方面进行了比较。所开发模型的测试结果表明，这四个模型的总正确分类率为83.7％至93.0％。每个分类模型都显示出自己的优势。 LRM通过调整截止值来提供灵活性，而RFM在这四个模型中显示出最高的准确性。通过分析预测模型中的重要变量并通过现场监测确定影响因素，可以解释粪便大肠菌的水生态过程。在模型和现场监测中，与降雨有关的变量，水坝流量和总磷均暴露出重要因素，这表明北汉江的粪便污染主要来自降雨事件和径流，包括农田和畜牧业的养分。位于Guwoon Creek和忠坪大坝的上游盆地。

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