Assessing Water Temperature and Dissolved Oxygen and Their Potential Effects on Aquatic Ecosystem Using a SARIMA Model
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".