Prediksi Luas Area Terbakar Menggunakan Fire Weather Index dan Frekuensi Titik Panas di Jambi
Bibliographic record
Abstract
Forest fires occur every year in Indonesia, one of the regions with the highest forest fires is Jambi Province. Significant losses and negative impacts due to forest fires cause the need for an effort to prevent forest fires early on with the detection of forest and land fires. One method that can provide information about the level of forest fire based on daily weather data input is the Fire Weather Index (Fire Weather Index / FWI) system, which was first developed by Canada. This study aims to estimate burn area in the Jambi region by using temperature, rainfall, humidity, and wind speed data. Other supporting data are hotspot frequency data from NASA-FIRMS satellites and data fraction of burn area from GFED satellites on a daily scale of the period 2006-2016. In this study an analysis of the relationship between these data variables and burn area estimation was carried out using multiple linear regression methods then validated to see the level of suitability of the output model forecasts. The results showed that the predictor variables that had the highest relationship were hotspots frequency, Buildup Index (BUI), and FWI index with correlation values of 0.888, 0.739 and 0.753, respectively. The estimation model of the resulting burnt area is: Burned Area = -966.6146918 + (7.519631195 × BUI) + (147.4865469 × FWI) + (14.5373858 × Hotspots) + 116, with an RMSE value of 635.524 and MAE of 491.38
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".