Enhancement of fire danger rating system for a better land/forest fire warning in South Sumatera Province, Indonesia
Bibliographic record
Abstract
Forest Fire Danger Rating System (FDRS) developed in Indonesia is based on the Canadian Forest Fires Danger Rating System. The Meteorology, Climatology, and Geophysics Agency operates and publishes a daily Fire Weather Index system on its website as part of the FDRS. The so-called SPARTAN system is based on weather elements of rainfall, air temperature, wind speed, and humidity and does not consider soil conditions. This research aims to improve the Fire Weather Index system by adding information on land conditions. In this study, the area of interest was South Sumatera Province of Indonesia and the period of analysis was 2019. The normalized difference polarization index (NDPI) derived from the Synthetic Aperture Radar (SAR) data of the Sentinel-1 satellite and land cover changes and fire incidents derived from the optical data of the Sentinel-2 satellite are used to represent land conditions. Since NDPI shows a good correlation with the degree of soil moisture, the NDPI is considered for the soil moisture conditions. Furthermore, integrating soil moisture conditions and land cover changes into the FWI system provides better early warning information for land/forest fires. Fire hotspot data and in-situ fire information are used to validate the results. This study concludes that adding information on land conditions will provide detailed and better land/forest fire warnings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".