Comparative Analysis of Artificial Neural Networks with Classical Regression Models for Predicting Dissolved Oxygen in Water
The increasing pollution of surface waters necessitates continuous monitoring of key environmental parameters to assess water quality. Dissolved Oxygen (DO) is a critical indicator of the health of aquatic ecosystems and is influenced by factors such as temperature, pH, and electrical conductivity....
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universidad De La Salle Bajío
2025-07-01
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| Series: | Nova Scientia |
| Subjects: | |
| Online Access: | https://novascientia.lasallebajio.edu.mx/ojs/index.php/novascientia/article/view/3651 |
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| Summary: | The increasing pollution of surface waters necessitates continuous monitoring of key environmental parameters to assess water quality. Dissolved Oxygen (DO) is a critical indicator of the health of aquatic ecosystems and is influenced by factors such as temperature, pH, and electrical conductivity. Traditional water quality models often require extensive datasets, which can imply challenges for large-scale monitoring. In this study, we evaluate the effectiveness of Artificial Neural Networks (ANNs) in predicting DO levels by comparing seven different ANN architectures to nine classical regression models. We utilize data from the Water Quality Prediction dataset available in the University of California at Irvine (UCI) Machine Learning Repository, which covers 37 geographic regions across the United States. Our analysis shows that a Multi-Layer Perceptron (MLP) outperforms traditional regression approaches. The MLP effectively captures complex, nonlinear relationships in water quality data, achieving higher predictive accuracy as measured by the coefficient of determination (R²). These findings emphasize the potential of deep learning methodologies as reliable alternatives to conventional statistical models for water quality prediction, offering improved accuracy and efficiency in environmental monitoring.
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| ISSN: | 2007-0705 |