Analisis Debit Banjir Rencana Daerah Tangkapan Air Waduk Tugu Menggunakan HEC-HMS
Bibliographic record
Abstract
Over the course construction of Tugu Reservoir, it is proposed to build a spillway gate to increase its performance as a flood control infrastructure. The construction planning of spillway gate requires a calculation of design flood discharge for Tugu Reservoir Catchment Area. The latest calculation was carried out during its construction planning in 2010. Therefore, it is necessary to evaluate and recalculate the design flood discharge using the latest data. This study aims to model design flood discharge of Tugu Reservoir Catchment Area using HEC-HMS software. This software is able to simulate rainfall-runoff modeling in a catchment area. Based on field conditions, Tugu Reservoir catchment has parameters, curve number value of 79, impervious value of 5%, and a lag time of 4.81 minutes. The result of the HEC-HMS modeling shows that the design flood discharge of Tugu Reservoir for Q100 is 369,30 m3/s; Q1000 is 656,70 m3/s; and QPMF is 995.30 m3/s. Based on the test using Creager Graph, the design flood discharge for Tugu Reservoir is still in the reasonable category with C value below 100. The result of the HEC-HMS modeling is not much different from the calculation result of Tugu Reservoir Construction Planning in 2010.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".