Real-time daily flow forecasting using black-box models, diffusion processes, and neural networks
Why this work is in the frame
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Bibliographic record
Abstract
The purpose of this study is to compare three modeling approaches used for the prediction of daily natural flows 1-7 days ahead. Linear black-box models, which have been commonly used for modeling flows, constitute the first approach. The second approach, a linear type in the context of our application, is less known in the water resources field and is identified by the term diffusion process. The third approach uses models called neural networks, which have gained interest in many fields. All these approaches were tested on 15 watersheds from the Saguenay - Lac-Saint-Jean hydrographic system, located in the province of Quebec, Canada. Because the watersheds possess different physical characteristics, the models were tested under several runoff conditions. In this article, the focus is on results; all approaches along with their conditions of use have been detailed elsewhere in the literature. The results obtained showed that neural networks constitute, for almost all the watersheds studied, the best approach to forecast daily natural flows. The more flexible structure of neural networks allows a best reproduction of complex runoff conditions. However, neural networks are more sensitive to outliers present in observed natural flow series, which are used as inputs in the three models tested. In practice, to model flows at specific periods of the year, it seems preferable to establish seasonal models. If a neural network has an inadequate structure for the period under consideration, then it may produce less convincing results than the other two modeling approaches tested in this study.Key words: forecasts, flows, black-box model, diffusion process, neural network.
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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) | 0.001 | 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 it