Lake Michigan Beaches’ E. Coli Prediction Tool Using Artificial Neural Network
Publication: World Environmental and Water Resources Congress 2023
ABSTRACT
Indiana Department of Environmental Management (IDEM) does daily water samples in southern Lake Michigan beaches daily during regular beachgoer season. This data is used to close the beaches for E. coli spikes. One-day lag data are used for beach closures on a given day. This research is an effort to create a model with a recent tool to predict E. coli classes using other physical, chemical, and site conditions so that it becomes a tool to avoid one-day time lag. In 2019, during regular beachgoer season, data samples were collected and documented to develop an artificial neural network model. Different combinations were tried, and the best model was identified after cross validation. BRNN (Bayesian regularization neural network) was selected as the best model. This model used 13 input data to predict E. coli classes with a successful prediction of 80%–90% in training, testing, and validation data sets.
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Information & Authors
Information
Published In
World Environmental and Water Resources Congress 2023
Pages: 1113 - 1118
History
Published online: May 18, 2023
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Bacteria
- Beaches
- Bodies of water (by type)
- Business management
- Climates
- Coastal engineering
- Coasts, oceans, ports, and waterways engineering
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Environmental engineering
- Health hazards
- Lakes
- Methodology (by type)
- Neural networks
- Pollutants
- Practice and Profession
- Public administration
- Public health and safety
- Research methods (by type)
- Seasonal variations
- Shores
- Validation
- Water and water resources
- Water management
- Water quality
- Water treatment
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