Forest fire risk assessment with soil data in Croatia
Bibliographic record
Abstract
Forest fire research can comprise forest fire case studies, laboratory experiments, fire detection by ground sensors, unmanned aerial vehicles and satellites, development of fire behaviour models, fire danger forecast, fire risk assessment, and much more. Commonly used and accepted Canadian method for forest fire danger forecast is expressed as Fire Weather Index (FWI) uses weather data. The index estimates the danger of wildfire and is based on meteorological parameters (air temperature, air humidity, wind speed, and rainfall amount) referring to 12 UTC for that day at the meteorological station or on a numerical weather prediction model grid point. Knowing how weather and soil interact and affect each other, we propose a new fire risk index based on the innovative Soil Index. Using open-access data, we collected different soil data such as soil temperature and soil moisture, land cover, vegetation, slope, etc. Since there are different types of vegetation and states, Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI) are considered as well. Being focused on forest fires, data about the burned area were also taken into account as well as the slope of the terrain for which the fire risk is calculated. Since all mentioned data have a diverse horizontal and temporal resolution, we decided to group them by temporal resolution: static, semi-static, and dynamic data. Static data refers to data that rarely change (never or every few years; e.g. land cover). Semi-static data refers to data that vary weekly or monthly (e.g. LAI). Dynamic data group refers to data that is strongly influenced by weather conditions (like soil temperature) and varies every hour. Because of various horizontal resolutions, soil parameters are interpolated to the same horizontal grid. Soil parameters are analysed concerning historical forest fires in Croatia. Despite Soil Index being based on soil parameters, we compared it with Fire Weather Index using data records for historical forest fires in Croatia. Obtained results indicate that the soil index has a better prediction performance compared to FWI. This study also highlights that not only the meteorological environment but also soil conditions are important parameters for fire risk assessment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".