Climate Indices Impact in Monthly Streamflow Series Forecasting
Bibliographic record
Abstract
Hydroelectricity has been widely deployed in many countries for decades, and remains the main source of electricity in Canada, Norway, and Brazil. Despite the recent diversification, hydroelectricity accounts for approximately 65% of the electricity generated in Brazil. It also represents the largest non-polluting source in the country, and is important for complying with the global goals of reducing carbon emissions. Meeting energy and environmental sustainability requirements impose challenges on the hydroelectric sector in terms of resilience to climate change observed around the planet. These changes affect electricity generation by altering seasonality and increasing the variability of streamflow and evaporation losses in reservoirs. Therefore, in this study, among a set of 27 climate indices, we identified the most relevant for improving the performance of the models applied to monthly seasonal streamflow series forecasting. A database provided by the NOAA Physical Sciences Laboratory was used as exogenous variables for three machine learning models (support vector regression, extreme learning machine, and kernel ridge regression) and one linear model (seasonal autoregressive integrated moving average with exogenous factors, SARIMAX). Random forest with recursive feature elimination was used as the feature-selection technique. The results obtained allowed for the identification of the most relevant set of indices for the analyzed plants, thereby improving streamflow predictions.
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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 | 0.002 | 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) | 0.001 | 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".