Predicting Escherichia coli loads in cascading dams with machine learning: An integration of hydrometeorology, animal density and grazing pattern.

Autor: Abimbola OP; Department of Biological Systems Engineering, University of Nebraska-Lincoln, 223 L. W. Chase Hall, Lincoln, NE 68583-0726, United States., Mittelstet AR; Department of Biological Systems Engineering, University of Nebraska-Lincoln, 223 L. W. Chase Hall, Lincoln, NE 68583-0726, United States. Electronic address: amittelstet2@unl.edu., Messer TL; Department of Biological Systems Engineering, University of Nebraska-Lincoln, 223 L. W. Chase Hall, Lincoln, NE 68583-0726, United States; Conservation and Survey Division, School of Natural Resources, University of Nebraska-Lincoln, 101 Hardin Hall, 3310 Holdrege Street, Lincoln, NE 68583-0996, United States., Berry ED; USDA Meat Animal Research Center, P.O. BOX 166, (State Spur 18D)/USDA-ARS-PA-MARC, Clay Center, NE 68933, United States., Bartelt-Hunt SL; Department of Civil and Environmental Engineering, University of Nebraska-Lincoln, 1110 S. 67th St., Omaha, NE 68182-0178, United States., Hansen SP; Department of Biological Systems Engineering, University of Nebraska-Lincoln, 223 L. W. Chase Hall, Lincoln, NE 68583-0726, United States.
Jazyk: angličtina
Zdroj: The Science of the total environment [Sci Total Environ] 2020 Jun 20; Vol. 722, pp. 137894. Date of Electronic Publication: 2020 Mar 12.
DOI: 10.1016/j.scitotenv.2020.137894
Abstrakt: Accurate prediction of Escherichia coli contamination in surface waters is challenging due to considerable uncertainty in the physical, chemical and biological variables that control E. coli occurrence and sources in surface waters. This study proposes a novel approach by integrating hydro-climatic variables as well as animal density and grazing pattern in the feature selection modeling phase to increase E. coli prediction accuracy for two cascading dams at the US Meat Animal Research Center (USMARC), Nebraska. Predictive models were developed using regression techniques and an artificial neural network (ANN). Two adaptive neuro-fuzzy inference system (ANFIS) structures including subtractive clustering and fuzzy c-means (FCM) clustering were also used to develop models for predicting E. coli. The performances of the predictive models were evaluated and compared using root mean squared log error (RMSLE). Cross-validation and model performance results indicated that although the majority of models predicted E. coli accurately, ANFIS models resulted in fewer errors compared to the other models. The ANFIS models have the potential to be used to predict E. coli concentration for intervention plans and monitoring programs for cascading dams, and to implement effective best management practices for grazing and irrigation during the growing season.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2020 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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