Predicting Fecal-Indicator Organisms in Coastal Waters Using a Complex Nonlinear Artificial Intelligence Model
Publication: Journal of Environmental Engineering
Volume 149, Issue 2
Abstract
High levels of fecal-indicator organisms (FIOs) at bathing water sites can cause disease and impose threats to public health. There is a need for predicting FIO levels to inform the public and reduce exposure. Data-driven models are one of the main tools being considered as predictive models. However, identifying the main inputs of the data-driven models is a major challenge in developing FIO predictor models. This paper develops a data-driven model for FIO concentration prediction based on a limited number of critical input variables. Essential variables were identified with be a combination of the gamma test and Genetic Algorithm (Gamma-GA test). Artificial neural networks (ANNs) and linear regression models were developed using these two variable identification approaches for comparison. The models were applied to a case study, and it was found that the model using the Gamma-GA test has a high potential to predict FIO levels more accurately, although this requires further investigation with different case studies. A correlation analysis was required prior to the variable identification approaches in this study. The need of this analysis highlights the significance of understanding the waterbody and the data set in the development and application of data-driven models. Models using a Gamma-GA test were more capable of predicting extreme (high) FIO concentrations, making a Gamma-GA test more suitable for a bathing water quality early warning system. The importance of nonlinearity in such predictive models was also demonstrated by the better performance of nonlinear ANN models compared with linear regression models regardless of the variable identification approaches used. This paper highlights the importance of nonlinearity in bathing water quality prediction and encourages further utilization of nonlinear models for this application.
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Data Availability Statement
Some data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request (MATLAB codes for Gamma-GA tests and the ANN models and model outputs). The bacteria and environmental data were collected by Aberystwyth University, Swansea City Council, and Natural Resources Wales (Environmental Protection and Regulatory Authority in Wales), and unfortunately, the authors have not been authorized to share the data set.
Acknowledgments
The study is carried out as a part of the EERES4WATER (Promoting Energy-Water nexus resource efficiency through renewable energy and energy efficiency) project, which is co-financed by the Interreg Atlantic Area Programme through the European Regional Development Fund under EAPA 1058/2018.
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Information & Authors
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Published In
Journal of Environmental Engineering
Volume 149 • Issue 2 • February 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Apr 13, 2022
Accepted: Oct 5, 2022
Published online: Dec 6, 2022
Published in print: Feb 1, 2023
Discussion open until: May 6, 2023
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Business management
- Case studies
- Computer programming
- Computing in civil engineering
- Decision making
- Decision support systems
- Engineering fundamentals
- Environmental engineering
- Methodology (by type)
- Model accuracy
- Models (by type)
- Neural networks
- Organisms
- Pollutants
- Practice and Profession
- Public administration
- Public health and safety
- Research methods (by type)
- Water quality
- Water treatment
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