Updating inflow forecasts using empirical statistical matching for real-time prediction of daily net inflows to Okanagan Lake
Bibliographic record
Abstract
Accurate predictions of flow periods are important for decision-making within the Okanagan Lake System. A nonparametric method to predict the hydrograph to achieve a closer match with the timing and volume of reservoir inflows during the dominant flow period (February1 to July 31) in Okanagan Lake was developed in this study. The method employed, Real-Time Statistical Matching (RTSM), uses a combination of information from a changing suite of best-fit historical years, existing forecasts, and recent inflow trends. This included a comparison between the current hydrograph against hydrographs derived from historical inflows based on the predicted volume and pattern of the hydrograph. The RTSM-based approach is hypothesized to improve the ability of hydrological models to predict shifts in the general timing of peak net inflows. This makes the RTSM model more robust to both historic and non-historic conditions. The performance of the RTSM-based predictions was compared to the legacy hydrology model based on average timing of historic flows. Results indicate an improvement in predictive accuracy of 10%, 6%, and 80% for Nash-Sutcliffe Efficiency (NSE), root mean squared error to standard deviation ratio (RSR), and percent bias (PBIAS) respectively, which are three different measures of the accuracy of predictions. Further, the success of the Okanagan Fish/Water Management Tool (FWMT) relies on the water and fish managers that use the tool, which extends beyond the quantitative metrics in this study. The authors’ also discussed how the tool’s utility has changed over time from when it was put into practice. It was learned that in practice, the best use of the model was based on the volume-based prediction with the real-time adjustment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".