Abstract
Temperature and dissolved oxygen (DO) are of critical importance for sustainable aquatic ecosystem and biodiversity in the river systems. This study aims to develop a data-driven model for forecasting water quality in the Athabasca River using a seasonal autoregressive integrated moving average model (SARIMA) for forecasting monthly DO and water temperature. DO and water temperature observed at Fort McMurray and Athabasca from 1960 to 2023 were used to train and test the model. The results show the satisfied model performance of DO with a coefficient of determination (R2) value of 0.76 and an RMSE value of 0.79 for training and 0.67 and 0.92 for testing, respectively, at the Fort McMurray station. At the Town of Athabasca station, the RMSE and R2 of DO were 0.92 and 0.72 for training and 0.77 and 0.86 for testing, respectively. For the modeled temperature, RMSE and R2 were 2.7 and 0.87 for training and 2.2 and 0.95 for testing, respectively, at Fort McMurray and were 2.0 and 0.93 for training and 1.8 and 0.97 for testing, respectively, in the Town of Athabasca. The results show that DO concentration is inversely proportional to the temperature. This implies that the DO could be related to water temperature, which, in turn, is correlated with air temperature. Therefore, the SARIMA model performed reasonably well in representing the dynamics of water temperature and DO in the cold climate river. Such a model can be used in practice to reduce the risk of low DO events.
| Original language | English |
|---|---|
| Article number | 25 |
| Journal | Environments - MDPI |
| Volume | 12 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan. 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- SARIMA model
- aquatic ecosystem
- dissolved oxygen
- machine learning
- water quality
- water temperature
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