Towards machine learning for the estimation of wildfire risk from weather and sociological data
Bibliographic record
Abstract
Estimating the probability of a wildfire occurring at a specific location on a given day comes with the challenge that it not only depends to a high degree on weather conditions and soil moisture, but also on the presence of an ignition source [1]. A commonly used index to assess wildfire risks is the Canadian Fire Weather Index [2], which does, however, not model the presence of an ignition source. We develop a machine learning model which discriminates between (1) the probability of a wildfire occurring given an ignition source, and (2) the probability of an ignition source being present, and inferences both. We first demonstrate the performance of our approach by estimating these probabilities on simulated data. With these simulations, we also assess the robustness of our model to machine learning-related challenges that arise with wildfire data, such as extreme class imbalance and label uncertainty. We then show the performance of our model trained on satellite-derived global wildfire occurrences between 2001 and 2017. The dataset FireTracks, which includes a comprehensive record of wildfire occurrences [3], is used as ground truth. Input features include weather data (ERA5 [4]) and population densities (GPW4 [5]). Finally we compare wildfire risk ratings computed with the Canadian Fire Weather Index to the probabilities estimated by our model. References [1] K. Rao et al., SAR-enhanced mapping of live fuel moisture content, Remote Sensing of Environment, 2020. [2] R. D. Field et al., Development of a Global Fire Weather Database. Natural Hazards and Earth System Sciences, 2015. [3] D. Traxl, FireTracks Scientific Dataset, 2021. (https://github.com/dominiktraxl/firetracks) [4] H. Hersbach et al., ERA5 hourly data on single levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2018. [5] Center for International Earth Science Information Network - CIESIN - Columbia University, Gridded Population of the World, Version 4 (GPWv4): Population Density, NASA Socioeconomic Data and Applications Center (SEDAC), 2016.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".