Predictive Modeling of River Water Temperatures in Catu River: A Neural Network-Based Approach
Predicting water temperature (Tw) in tropical environments is crucial for ecosystem monitoring and the sustainable management of water resources. Highly accurate and reliable Tw forecasts are essential for the ecological management of rivers. This study evaluates the performance of machine learning-...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/acis/8810911 |
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| author | Carmen Goncalves de Macedo e Silva José Roberto de Araújo Fontoura Alarcon Matos de Oliveira Thais de Souza Neri Roberto Luiz Souza Monteiro Thiago Barros Murari Alexandre do Nascimento Silva Leandro Brito Santos Marcos Batista Figueredo |
| author_facet | Carmen Goncalves de Macedo e Silva José Roberto de Araújo Fontoura Alarcon Matos de Oliveira Thais de Souza Neri Roberto Luiz Souza Monteiro Thiago Barros Murari Alexandre do Nascimento Silva Leandro Brito Santos Marcos Batista Figueredo |
| author_sort | Carmen Goncalves de Macedo e Silva |
| collection | DOAJ |
| description | Predicting water temperature (Tw) in tropical environments is crucial for ecosystem monitoring and the sustainable management of water resources. Highly accurate and reliable Tw forecasts are essential for the ecological management of rivers. This study evaluates the performance of machine learning-based predictive models in forecasting Tw in the Catu River. The models were trained using climatic and hydrological data collected from 2009 to 2016 and validated with real data from 2023. The evaluated models include backpropagation neural network (BPNN), Random Forest, Bidirectional LSTM (BiLSTM), Air2Stream, and NARX, employing nine input variables such as atmospheric pressure, air temperature, and water vapor concentration. The results show that the BiLSTM model achieved the best performance, with a root mean square error (RMSE) of 0.12°C and R2 = 0.98, followed by BPNN with an RMSE of 0.18°C and R2 = 0.91, and the Random Forest model, which obtained an NSE of 0.95. These models demonstrated a strong ability to predict Tw under both normal and extreme conditions, capturing the thermal dynamics of the Catu River with high precision during events involving minor thermal variations. Conversely, the NARX and Air2Stream models exhibited lower performance, proving more prone to errors under conditions of extreme variability. The findings of this study provide valuable scientific insights for river Tw prediction and the protection of aquatic ecosystems, with practical applications in water resource management in tropical regions. |
| format | Article |
| id | doaj-art-72bb5d4a222440f0866f6541cf85c4de |
| institution | DOAJ |
| issn | 1687-9732 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-72bb5d4a222440f0866f6541cf85c4de2025-08-20T03:09:12ZengWileyApplied Computational Intelligence and Soft Computing1687-97322025-01-01202510.1155/acis/8810911Predictive Modeling of River Water Temperatures in Catu River: A Neural Network-Based ApproachCarmen Goncalves de Macedo e Silva0José Roberto de Araújo Fontoura1Alarcon Matos de Oliveira2Thais de Souza Neri3Roberto Luiz Souza Monteiro4Thiago Barros Murari5Alexandre do Nascimento Silva6Leandro Brito Santos7Marcos Batista Figueredo8Modeling and Simulations of BiosystemsModeling and Simulations of BiosystemsModeling and Simulations of BiosystemsModeling and Simulations of BiosystemsComputational ModelingIndustrial Management and TechnologyModeling and Simulations of BiosystemsCenter for Science and Technology in Energy and SustainabilityModeling and Simulations of BiosystemsPredicting water temperature (Tw) in tropical environments is crucial for ecosystem monitoring and the sustainable management of water resources. Highly accurate and reliable Tw forecasts are essential for the ecological management of rivers. This study evaluates the performance of machine learning-based predictive models in forecasting Tw in the Catu River. The models were trained using climatic and hydrological data collected from 2009 to 2016 and validated with real data from 2023. The evaluated models include backpropagation neural network (BPNN), Random Forest, Bidirectional LSTM (BiLSTM), Air2Stream, and NARX, employing nine input variables such as atmospheric pressure, air temperature, and water vapor concentration. The results show that the BiLSTM model achieved the best performance, with a root mean square error (RMSE) of 0.12°C and R2 = 0.98, followed by BPNN with an RMSE of 0.18°C and R2 = 0.91, and the Random Forest model, which obtained an NSE of 0.95. These models demonstrated a strong ability to predict Tw under both normal and extreme conditions, capturing the thermal dynamics of the Catu River with high precision during events involving minor thermal variations. Conversely, the NARX and Air2Stream models exhibited lower performance, proving more prone to errors under conditions of extreme variability. The findings of this study provide valuable scientific insights for river Tw prediction and the protection of aquatic ecosystems, with practical applications in water resource management in tropical regions.http://dx.doi.org/10.1155/acis/8810911 |
| spellingShingle | Carmen Goncalves de Macedo e Silva José Roberto de Araújo Fontoura Alarcon Matos de Oliveira Thais de Souza Neri Roberto Luiz Souza Monteiro Thiago Barros Murari Alexandre do Nascimento Silva Leandro Brito Santos Marcos Batista Figueredo Predictive Modeling of River Water Temperatures in Catu River: A Neural Network-Based Approach Applied Computational Intelligence and Soft Computing |
| title | Predictive Modeling of River Water Temperatures in Catu River: A Neural Network-Based Approach |
| title_full | Predictive Modeling of River Water Temperatures in Catu River: A Neural Network-Based Approach |
| title_fullStr | Predictive Modeling of River Water Temperatures in Catu River: A Neural Network-Based Approach |
| title_full_unstemmed | Predictive Modeling of River Water Temperatures in Catu River: A Neural Network-Based Approach |
| title_short | Predictive Modeling of River Water Temperatures in Catu River: A Neural Network-Based Approach |
| title_sort | predictive modeling of river water temperatures in catu river a neural network based approach |
| url | http://dx.doi.org/10.1155/acis/8810911 |
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