Effects of meteorology on forest fires in India: A modeling study
Bibliographic record
Abstract
Meteorology has a strong impact on forest fires. Meteorological parameters such as temperature, relative humidity, wind speed and precipitation alter the fuel loading in forests, control the changes in spatial distribution, intensity and frequency of forest fires and changes in forest fire season. Hence, it is important to understand the relationship between forest fires and meteorological factors and build models that can simulate these relationships. The Canadian Forest Fire Danger Rating System (CFFDRS) has been used globally to assess and predict the fire behavior in various forest ecosystems. The Fire Weather Index (FWI) of CFFDRS models the relationship between meteorology and forest fires. In this study we calibrate the FWI over Indian forests using percentile analysis and logistic regression technique and test the performance using satellite-derived (MODIS daily fire data from 2003-2018) fire count and Fire Radiative Power (FRP). As the Indian forest landscape is highly heterogeneous, we calibrate the FWI over 4 FWI zones namely Himalayan, Deciduous, Western Ghats and Thorn forests based on IGBP forest classification and Koppen climatic zones. Five fire danger classes having thresholds of 99th, 95th, 90th, 80th and 70thof FWI percentiles have been defined with decreasing severity. Results show that the calibrated FWI is capable of simulating the forest fire behavior over India. Sensitivity studies show that temperature and relative humidity are the key controlling factors of forest fires over India. This study is one of the first attempts to use fire models to simulate fire behavior over India. It can serve as a launchpad for further work on fire hazard prediction and effects of climate change on fire hazard in India.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".