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

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 co

SUBMITTER: Chen F 

PROVIDER: S-EPMC10206036 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature