Predicting bacterial transport through saturated porous media using an automated machine learning model

Frontiers in Microbiology

Microbiology

Jie Zhuang¹  Bin Zhou²  Liqiong Yang³  Fengxian Chen³  Xijuan Chen³ 

[1] Department of Biosystems Engineering and Soil Science, Center for Environmental Biotechnology, The University of Tennessee, Knoxville, TN, United States;Faculty of Medicine, University of Augsburg, Augsburg, Germany;Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning, China;

关键词: bacterial transport;    automated machine learning;    first-order attachment coefficient;    spatial removal rate;    machine learning;   

DOI  :  10.3389/fmicb.2023.1152059

 received in 2023-01-27, accepted in 2023-04-25,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Chen, Zhou, Yang, Chen and Zhuang.