Real-time short-term natural water inflows forecasting using recurrent neural networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accurate, time and site-specific forecasts of natural inflows into hydropower reservoirs are highly important for operating and scheduling. This paper investigates the effectiveness of recurrent neural networks (RNN) for real-time short-term natural water inflows forecasting. The models use antecedent inflows and precipitation data, and actual weather descriptors to generate short-term (1-7 days ahead) natural inflow forecasts for a specific hydroelectric reservoir. The input variables are exactly the same as those previously used for an autoregressive moving average model with exogenous variables (ARMAX) and for a conceptual model (PREVIS). The RNN are trained using the early stopped training technique with the Levenberg-Marquardt backpropagation. The experimental results show that the performance of RNN using the early stopped training approach outperforms the traditional stochastic model and the available conceptual model. Particularly, the RNN have shown better forecasting capabilities for the last 3 of the seven days ahead forecasts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it