ML-based fire cause prediction
Integrating population mobility and meteorological data
Bibliographic Data
| ID | 22025159 |
|---|---|
| Authors | David Sládek (0000-0002-4418-3203, University of Defence), Filip Dohnal (0000-0002-4908-6357, Tomas Bata University in Zlin, corresponding author), František Paulus (0000-0002-8199-7548), Jiřı́neubauer (0000-0003-2497-2739, University of Defence), Tomáš Zeman (0000-0002-2953-4925, Tomas Bata University in Zlin) |
| Year | 2026 |
| Volume | 143 |
| Pages | 106257 |
| Publication date | 2026-09-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Disaster Risk Reduction (JOURNAL) |
| Journal identifiers | ISSN: 2212-4209 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ijdrr.2026.106257 |
| OpenAlex | W7164584725 |
| Language | EN |
| References cited | 22 |
Wildfire management in Central Europe is often hampered by incomplete data; in the Czech Republic, nearly 49% of fire incidents are recorded with "Unknown" causes. This study presents a novel machine learning framework to address this gap by integrating Global Forecast System (GFS) meteorological data, geographic variables, and dynamic population mobility derived from aggregated SIM-card movement. Utilizing a Gradient Boosting model, we achieved an accuracy of up to 53% in predicting ignition causes, audited by a Natural Language Processing (NLP) semantic proxy of emergency call logs, indicating alignment with model predictions in 38–50% of "Unknown" cases, suggesting a recoverable lower bound of latent information in otherwise unstructured data. Variation in predictive accuracy may reflect differences in the underlying structure and heterogeneity of fire occurrence processes, although it is also influenced by data quality, class imbalance, and model limitations. Through hierarchical clustering, we demonstrate that fires in Seniors-Dominated, stable rural communities are the most predictable, while dynamic urban environments introduce stochastic social noise that reduces model performance. These results offer a dual-purpose tool for fire departments: a method for retrospective database population and a framework for localized, demographic-specific risk management
Geographic information system · Geographic mobility · Gradient boosting · Population · Fire Detection and Safety Systems · Fire effects on ecosystems · Injury Epidemiology and Prevention
Stanza
Fire-smart solutions for sustainable wildfire risk prevention
Integrating physical drivers for wildfire hazard modelling in support of disaster risk reduction
Assessment of social vulnerability to forest fire and hazardous facilities in Germany
Understanding and modelling wildfire regimes
Integrating hydrological parameters in wildfire risk assessment
The distributional incidence of wildfire hazard in the western United States
The fire weather in Europe
Circumpolar spatio-temporal patterns and contributing climatic factors of wildfire activity in the Arctic tundra from 2001–2015
| Citation velocity | historical |
|---|---|
| Highly cited | No |