Analisis Tren Perubahan Intensitas Hujan (Studi Kasus: Jakarta dan Bogor)
Bibliographic record
Abstract
Rainfall intensity and drainage channel capacity are the main inputs in flood management infrastructure planning. The Intensity-Duration-Frequency (IDF) curve in a region has a different pattern from other regions. This study examines the temporal distribution pattern of rain and the IDF curve with case studies of the Jakarta and Bogor, represented by Kemayoran Rainfall Station and Citeko Rainfall Station, respectively. This study compared data analysis from the above stations with the ABM Method, Modified Mononobe, Indonesian National Standard for Calculating Flood Discharge, and Indonesian National Standard for Road Surface Drainage Design. The analysis results showed that the rainfall event in the Jakarta and Bogor areas was dominated by events of ≤ 4 hours with the highest percentage of rainfall volume at the beginning of the event (at the first two hours of the event). The observed hourly rainfall data has a significant difference pattern with the rainfall distribution calculated using the ABM and Modified Mononobe methods. The IDF curve obtained from the Base Curve on the Indonesian National Standard for Road Surface Drainage Design gives higher results than the IDF curve of the observation data. Meanwhile, the IDF curve calculated using the Mononobe Equation gives lower results than the IDF curve of the observation data. This study shows that standard design of rainfall duration and rainfall intensity for drainage infrastructure planning and flood management need to be continuously developed based on the latest observational data with better temporal and spatial resolution. Continuous effort in improving codes and design standard needs to be conducted to respond to climate change challenges, dynamic regional developments, and the increasing trend of flood events in Indonesia.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".