A Methodology for Forecasting Dissolved Oxygen in Urban Streams
Bibliographic record
Abstract
Real-time monitoring of river water quality is at the forefront of a proactive urban water management strategy to meet the global challenge of vital freshwater resource sustainability. The concentration of dissolved oxygen (DO) is a primary indicator of the health state of the aquatic habitats, and its modeling is crucial for river water quality management. This paper investigates the importance of the choices of different techniques for preprocessing and stochastic modeling for developing a simple and reliable linear stochastic model for forecasting DO in urban rivers. We describe several methods of evaluation, preprocessing, and modeling for the DO parameter time series in the Credit River, Ontario, Canada, to achieve the optimum data preprocessing and input selection techniques and consequently obtain the optimum performance of the stochastic models as an effective river management tool. The Manly normalization and standardization (Std) methods were chosen for preprocessing the time series. Modeling the preprocessed time series using the stochastic autoregressive integrated moving average (ARIMA) model resulted in very accurate forecasts with a negligible difference from sole normalization and spectral analysis (Sf) methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".