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

  • 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, usingEscherichia 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.show moreshow less

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Metadaten
Author:Fengxian Chen, Bin ZhouORCiDGND, Liqiong Yang, Xijuan Chen, Jie Zhuang
URN:urn:nbn:de:bvb:384-opus4-1050846
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/105084
ISSN:1664-302XOPAC
Parent Title (English):Frontiers in Microbiology
Publisher:Frontiers Media SA
Place of publication:Lausanne
Type:Article
Language:English
Year of first Publication:2023
Publishing Institution:Universität Augsburg
Release Date:2023/06/22
Tag:Microbiology (medical); Microbiology
Volume:14
First Page:1152059
DOI:https://doi.org/10.3389/fmicb.2023.1152059
Institutes:Medizinische Fakultät
Medizinische Fakultät / Lehrstuhl für Model-based Environmental Exposure Science
Nachhaltigkeitsziele
Nachhaltigkeitsziele / Ziel 6 - Sauberes Wasser und Sanitäre Einrichtungen
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)