Tự động dò tìm bộ thông số tối ưu của mô hình thuỷ văn HEC–HMS bằng thuật toán SCE–UA
Bibliographic record
Abstract
Verification and calibration of the HEC-HMS hydrological model using a trialand-error procedure usually costs modelers a lot of time and effort and more importantly, the set of parameters found is often not optimal. This paper presents the results of developing an automatic program that allows the automatic search of the optimal set of parameters of the HEC-HMS model based on the SCE-UA algorithm. First, the Latin Hypercube sampling method is used to efficiently obtain the parameter values widely across the feasible solution space. Then the SCE-UA method is used to search for the optimal solution through complex shuffling and evolution based on the initial sampling values from the Latin Hypercube Sampling method. The problem is usually multi-objective, so the optimal solution is therefore selected based on a Pareto front and evaluated for uncertainty by the GLUE method. The program has been and is being applied to the Krng H'nng hydropower reservoir in Dak Lak province. The authors use real data measured from 18 out of 33 floods observed in the period 2016-2021 to narrow the initial feasible solution space and to reduce the parameter dimensions (from 50 to 18) by identifying the three governing parameters , , and through sensitivity analysis. This helps to enhance the search speed and the convergence of the optimal solution. Based on this result, the program searches the optimal value for the parameters and updates them automatically in the realtime forecast. The results of the validation of the next 5 floods and testing of the remaining 10 floods give good results up to the time step + 4 hours. The evaluation indicators are high ( > 0.85, volume error < 10 %) and the result is always in the confidence range Q5%-Q95%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.175 | 0.043 |
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".