Evaluation of E. coli in Sediment for Assessing Irrigation Water Quality using Machine Learning

Authors:
Jennifer Duan
Owners: Jennifer Duan
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Created: May 15, 2022 at 4:39 a.m.
Last updated: May 15, 2022 at 4:56 a.m.
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Abstract

Fresh produce irrigated with contaminated water poses a substantial risk to human health. This study evaluated the impact of incorporating sediment information on improving the performance of machine learning models to quantify E. coli level in irrigation water. Field samples were collected from irrigation canals in the Southwest U.S., for which meteorological, chemical, and physical water quality variables as well as three additional flow and sediment properties: the concentration of E. coli in sediment, sediment median size, and bed shear stress. Water quality was classified based on E. coli concentration exceeding two standard levels: 1 E. coli and 126 E. coli colony forming units (CFU) per 100 ml of irrigation water. Two series of features, including (FIS) and excluding (FES) sediment features, were selected using multi-variant filter feature selection. The correlation analysis revealed the inclusion of sediment features improves the correlation with the target standards compared to the models excluding these features. Support vector machine, logistic regression, and ridge classifier were tested in this study. The support vector machine model performed the best for both targeted standards. Besides, incorporating sediment features improved all models’ performance. Therefore, the concentration of E. coli in sediment and bed shear stress are major factors influencing E. coli concentration in irrigation water.

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Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
Arizona Department of Agriculture Canal Sediments as Reservoirs of Pathogenic Bacteria in Irrigation Systems SCBGP19-28

How to Cite

Duan, J. (2022). Evaluation of E. coli in Sediment for Assessing Irrigation Water Quality using Machine Learning, HydroShare, http://www.hydroshare.org/resource/acc65e3482334e538fcbe5bc902a030e

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
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