Seasonal reservoir inflow forecasting with low-frequency climatic indices: a comparison of data-driven methods
Bibliographic record
Abstract
This paper investigates the potential of using data-driven methods, namely Bayesian neural networks (BNN), recurrent multi-layer perceptrons (RMLP), time-lagged feed-forward networks (TLFN), and conventional multi-layer perceptrons (MLP) to forecast seasonal reservoir inflows of the Churchill Falls watershed in northeastern Canada. A climate variability indicator (the El Niño-Southern Oscillation, ENSO) is used as additional information to historical inflow time series in order to predict seasonal reservoir inflows. The prediction results showed that the Bayesian neural network model was best able to capture the additional information provided by the ENSO series, and provided improved predictions in spring and summer seasons relative to the same model using only reservoir inflows. Similarly, time-lagged feed-forward networks and recurrent multi-layer perceptrons showed some improved forecast skill in spring when the ENSO index series are used but generally provided superior performance overall. The conventional multi-layer perceptron appears unable to capture relevant information from the ENSO series regardless of the season. However, when only historical flow series are used, all the selected data-driven methods provide very competitive forecast performances.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".