Accuracy Assessment of Chow's Regression and Stochastic Methods for Estimating Instantaneous Peak Discharge (Case Study: Central Alborz Region)
Bibliographic record
Abstract
In this research, accuracy of chow’s regression and stochastic methods was analyzed for estimating instantaneous peak discharge in central Alborz region, Iran. Instantaneous peak discharges data in this region were incomplete, so we were used daily peak flood data for completing Instantaneous peak discharges using regression method. Finally 23 gauge stations with 20 years common data selected for analysis. Used 7 important frequency distributions including, Normal, two parameters Log Normal, three parameters Log Normal, Two parameters Gama, Pearson type three, Log Pearson type three and Gumbel. Then the best distribution was chosen to estimating instantaneous peak discharges for 2, 5, 10, 15, 20, 25, 30, 50 and 100 years return periods. Instantaneous Peak discharges for above return periods were estimated using chow’s regression and Stochastic methods, and were compared with the best fitted distributions results using probabilities indices such as MSE and MBE. Our results showed that chow’s regression method is better than stochastic method for estimating instantaneous peak discharge in central Alborz region.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".