Numerical Simulation of Flood Propagation in the Kelara River Flood Early Warning System
Bibliographic record
Abstract
Flood historical data from the Kelara River in the last 10 years shows that the river has often overflowed, and the worst floods happened on January 22, 2019. One of the efforts to minimize the negative impact of a flood disaster is to conduct flood tracking. Flood tracking is an analysis of the flood along the river, or also known as flood propagation, which can be used as a reference in the preparation of a flood early warning system. This study aims to determine the propagation of the Kelara River flood which can be used to determine flood-prone areas and as a reference in the preparation of a flood early warning system. This research was carried out in 3 stages, namely flood hydrology analysis using the HEC-HMS program, numerical simulation of 2D floods using the HEC-RAS program, spatial modeling of flood-prone areas using the ArcGIS program, and preparation of a flood early warning system. The results of this study showed that the flood that occurred on January 22, 2019, was a 100-year return period flood, and determined that 10 points of residential areas/villages must be alerted when the intensity of rain is high, with the fastest time to be alerted being 52 minutes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".