Hydrometeorological Disaster Mitigation Through Rainfall Intensity Mapping Using IDF in Sumatera Island, Indonesia
Why this work is in the frame
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Bibliographic record
Abstract
Climate change due to weather anomalies causes rainfall and frequency to be more intense than normal conditions, including on the island of Sumatera, Indonesia.This has the potential to cause hydrometeorological disasters, such as floods and landslides.The intensity duration frequency (IDF) is an approach of rainfall amounts in water resources engineering for planning, designing, and operating water resources projects.Therefore, this article aims to conduct a rainfall intensity mapping study for specific return periods using the intensity duration frequency (IDF) approach, a case study of Sumatera Island, Indonesia, with the period of historical rainfall between 2013 and 2022.The purpose of the study is to provide essential reference information to mitigate hydrometeorological disasters to avoid the accumulation of rainwater in disaster-prone areas.The method used is a quantitative study using historical rainfall data from 48 rainfall recording stations in the Sumatera Island area.The results of this study show that the average maximum daily rainfall at nine stations is in the normal category, 39 rain gauge stations are in the standard medium rainfall category, and the rest are in the high category.Maps for each of these conditions are attached as the main results of this study.The practical application of this research has generated disaster vulnerability maps using IDF approaches for Sumatera Island (Indonesia) based on rainfall, emphasizing decreasing and aware activity in these areas.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it