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Tracking Major Sources of Water Contamination Using Machine Learning

Citation

Wu, Jianyong; Song, Conghe; Dubinsky, Eric A.; & Stewart, Jill R. (2021). Tracking Major Sources of Water Contamination Using Machine Learning. Frontiers in Microbiology, 11(3623), 616692. PMCID: PMC7854693

Abstract

Current microbial source tracking techniques that rely on grab samples analyzed by individual endpoint assays are inadequate to explain microbial sources across space and time. Modeling and predicting host sources of microbial contamination could add a useful tool for watershed management. In this study, we tested and evaluated machine learning models to predict the major sources of microbial contamination in a watershed. We examined the relationship between microbial sources, land cover, weather, and hydrologic variables in a watershed in Northern California, United States. Six models, including K-nearest neighbors (KNN), Naïve Bayes, Support vector machine (SVM), simple neural network (NN), Random Forest, and XGBoost, were built to predict major microbial sources using land cover, weather and hydrologic variables. The results showed that these models successfully predicted microbial sources classified into two categories (human and non-human), with the average accuracy ranging from 69% (Naïve Bayes) to 88% (XGBoost). The area under curve (AUC) of the receiver operating characteristic (ROC) illustrated XGBoost had the best performance (average AUC = 0.88), followed by Random Forest (average AUC = 0.84), and KNN (average AUC = 0.74). The importance index obtained from Random Forest indicated that precipitation and temperature were the two most important factors to predict the dominant microbial source. These results suggest that machine learning models, particularly XGBoost, can predict the dominant sources of microbial contamination based on the relationship of microbial contaminants with daily weather and land cover, providing a powerful tool to understand microbial sources in water.

URL

http://dx.doi.org/10.3389/fmicb.2020.616692

Reference Type

Journal Article

Article Type

Regular

Year Published

2021

Journal Title

Frontiers in Microbiology

Author(s)

Wu, Jianyong
Song, Conghe
Dubinsky, Eric A.
Stewart, Jill R.

PMCID

PMC7854693

Continent/Country

United States of America

State

California