Interpretability Versus Accuracy: A Comparison of Machine Learning Models Built Using Different Algorithms, Performance Measures, and Features to Predict E. coli Levels in Agricultural Water

Interpretability Versus Accuracy: A Comparison of Machine Learning Models Built Using Different Algorithms, Performance Measures, and Features to Predict E. coli Levels in Agricultural Water

Since E. coli is considered a fecal indicator in surface water, government water quality standards and industry guidance often rely on E. coli monitoring to identify when there is an increased risk of pathogen contamination of water used for produce production (e.g., for irrigation). However, studie...

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Bibliographic Details
Main Authors: Daniel L. Weller, Tanzy M. T. Love, Martin Wiedmann
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-05-01
Series:Frontiers in Artificial Intelligence
Subjects:
E. coli
machine learning
predictive model
food safety
water quality
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2021.628441/full

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