Hybrid Stochastic Model for Daily Flows Simulation in Semiarid Climates
Bibliographic record
Abstract
An innovative stochastic model for the purpose of daily flows simulations is presented in this paper. This model is particularly well suited for the generation of daily river flows in semiarid climates. The previous models have not preserved basic features of daily flow hydrographs, such as peaks and recessions, and have failed to reproduce the long low water and short flood periods due to the temporal distribution of precipitation in semiarid climates such as Sahelian regions. This hybrid stochastic model uses a product model that simulates an intermittent impulse series. The impulse magnitude is the flow increment from one day to the next. The intermittent impulse series enables the definition over time of alternate rising and falling limbs. On the rising limbs, the generated impulse magnitudes help to produce the daily flows. However, on the falling limbs, the flows are produced by a deterministic model using a time-varying recession coefficient that is, for the first time, introduced in stochastic modeling of daily flows. Application of this model for daily flows simulation at the Bakel Station on the Senegal River (West Africa) has provided good results as shown by the analysis of the synthetic daily flow series.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".