Inflow volume forecasting using regional deep learning models trained on operational meteorological ensemble forecasts in Canada
Bibliographic record
Abstract
Hydropower reservoirs typically require inflow forecasts to allow water resources managers to optimize drawdown rates and improve infrastructure efficiency. Usually, operators use physically-based or conceptual hydrological models to forecast streamflow for a desired lead-time, and then evaluate the total inflows for the period of interest. Recently, deep-learning models have been shown to provide better streamflow forecasts than classical hydrological models in certain cases. They have also shown better performance when trained on a multitude of donor catchments to increase the number of available data for training.This work presents a novel method to provide inflow forecasts volumes directly, i.e. without first generating day-to-day streamflow, by using a deep-learning model trained on ensemble meteorological forecasts and observed inflow volumes for given lead-times. Furthermore, the model makes use of large-scale datasets during its training, by including data from 200 catchments in Canada. The model is then applied to a hydropower system reservoir to estimate 14-day inflow volume forecasts. The model shows promising results in terms of accuracy and reliability, and it is demonstrated that the addition of extra donor catchments during training helps increase the forecast performance. Furthermore, training the model using forecasted meteorological data as the inputs helps further increase model performance. This work demonstrates the potential residing in training regional models using meteorological forecasts for ensemble inflow volumes forecasts.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".