PDF(Pubmed)
Abstract：
Escherichia coli, as an indicator of fecal contamination, can move from manure-amended soil to groundwater under rainfall or irrigation events. Predicting its vertical transport in the subsurface is essential for the development of engineering solutions to reduce the risk of microbiological contamination. In this study, we collected 377 datasets from 61 published papers addressing E. coli transport through saturated porous media and trained six types of machine learning algorithms to predict bacterial transport. Eight variables, including bacterial concentration, porous medium type, median grain size, ionic strength, pore water velocity, column length, saturated hydraulic conductivity, and organic matter content were used as input variables while the first-order attachment coefficient and spatial removal rate were set as target variables. The eight input variables have low correlations with the target variables, namely, they cannot predict target variables independently. However, using the predictive models, input variables can effectively predict the target variables. For scenarios with higher bacterial retention, such as smaller median grain size, the predictive models showed better performance. Among six types of machine learning algorithms, Gradient Boosting Machine and Extreme Gradient Boosting outperformed other algorithms. In most predictive models, pore water velocity, ionic strength, median grain size, and column length showed higher importance than other input variables. This study provided a valuable tool to evaluate the transport risk of E.coli in the subsurface under saturated water flow conditions. It also proved the feasibility of data-driven methods that could be used for predicting other contaminants\' transport in the environment.
摘要：
大肠杆菌,作为粪便污染的指标，在降雨或灌溉事件下，可以从粪便改良的土壤转移到地下水。预测其在地下的垂直运输对于开发工程解决方案以降低微生物污染的风险至关重要。在这项研究中,我们从61篇发表的关于大肠杆菌通过饱和多孔介质转运的论文中收集了377个数据集,并训练了6种类型的机器学习算法来预测细菌转运.八个变量,包括细菌浓度，多孔介质类型,中值晶粒尺寸，离子强度,孔隙水速度,柱长度,饱和导水率，以有机质含量为输入变量，以一阶附着系数和空间去除率为目标变量。八个输入变量与目标变量的相关性较低，即,他们不能独立预测目标变量。然而,使用预测模型，输入变量可以有效地预测目标变量。对于细菌保留率较高的情况，例如较小的中值晶粒尺寸，预测模型表现出更好的性能。在六种机器学习算法中，梯度提升机和极端梯度提升优于其他算法。在大多数预测模型中，孔隙水速度,离子强度,中值晶粒尺寸，列长度显示出比其他输入变量更高的重要性。这项研究为评估饱和水流条件下大肠杆菌在地下的运输风险提供了有价值的工具。它还证明了可用于预测环境中其他污染物传输的数据驱动方法的可行性。
