Hydrological Approach for Flood Overflow Estimation in Buleleng Watershed, Bali
Bibliographic record
Abstract
The Hydrology of a watershed, encapsulated within the biogeophysical characteristics of upstream, midstream, and downstream regions, governs the peak discharge.Notable flooding issues have been observed in the downstream region of the Buleleng Watershed, largely attributed to land use transitions from vegetative to built-up areas.This study seeks to delineate the flood overflow zone within the Buleleng River Basin, influenced by factors such as rainfall, land use, and soil texture.Data employed in this study encompass land use maps, an 8-meter resolution Digital Elevation Model, annual rainfall records, soil texture information, and river network maps.The adopted methodology involved field survey data collection, satellite image analysis, and hydrological approach computations.Peak discharge values for the Buleleng Watershed, derived for periods of 5, 10, 25, and 5-100 years, were determined to be 426334.44,568445.88,603035.31,and 617379 m 3 /s respectively, set against a river capacity of 312748.13m 3 /s.Given the substantial overflow discharge from the Buleleng River, the watershed is susceptible to flooding.The study findings indicate a progressive annual increase in flood overflow predictions, necessitating targeted interventions such as drainage management, surface runoff control, and a review of spatial permits for settlement usage, particularly within watershed conservation areas.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".