Machine learning based fire danger assessment framework for Indian forests 
Bibliographic record
Abstract
We developed a comprehensive fire risk assessment framework for Indian forests, divided into five distinct forest zones (Himalayan, Northeast, Central India, Deccan, and Western Ghats) characterized by diverse climatic conditions and forest types. This framework focused on three primary triggering factors: weather, fuel availability, and anthropogenic ignition.For the weather factor, we considered the Fire Weather Index (FWI) module of the Canadian Forest Fire Danger Rating System with ECMWF's ERA5 reanalysis as meteorological inputs over the period 2003-2021. As fire weather is a dominant factor in causing fires, we developed a robust system to predict fire weather danger. We evaluated the simulated FWI against MODIS active fire data and observed that FWI was a good enough metric for fire weather danger assessment. FWI was categorized into five danger classes through an ensemble approach based on logistic regression, FWI percentiles, percentage of fires, and K-means clustering. We introduced machine learning techniques to reduce the subjective decisions in these methods. This increased the efficiency of the danger rating system to detect fire probability well by 30-50%. A rigorous evaluation of the danger classes revealed that there was no overlap of central tendencies between different methods in the ensemble. The defined danger classes demonstrated coherent values for evaluative parameters, with a consistently high hit rate, low hits due to chance, moderate correct rejections, and an acceptable false alarm ratio.Addressing fuel availability, we used vegetation indices (MODIS normalized difference and enhanced vegetation indices) and topographic features (aspect, elevation and slope from FLDAS land surface model). The anthropogenic ignition factor consisted of population density and land use information. In India, fragmented forests cohabitate with human settlements and agricultural lands. To quantify the impact of anthropogenic ignition on fire occurrences, we computed the percentage of built-up and agricultural area within each grid cell. We used machine learning predictive algorithms such as multiple linear regression with interactions, support vector machines, decision trees and neural networks to integrate these triggering factors with fire count as the target variable, We selected the highest-performing system as the risk assessment framework.This country-scale fire risk assessment provides insights into regional exposure variations and serves as a foundational step towards establishing an operational fire risk assessment system for India. This framework will be of help to operational fire management agencies, enabling enhanced prediction of fire danger and informed decision-making.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 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".