25 citations as recorded by crossref.

1. Turbulence and fire-spotting effects into wild-land fire simulators I. Kaur et al. https://doi.org/10.1016/j.cnsns.2016.03.003
2. A Multi-observable Approach to Address the Ill-Posed Nature of Inverse Fire Modeling Problems M. Price et al. https://doi.org/10.1007/s10694-015-0541-7
3. An integrated approach for tactical monitoring and data-driven spread forecasting of wildfires M. Valero et al. https://doi.org/10.1016/j.firesaf.2017.03.085
4. Wind and Fire Coupled Modelling—Part I: Literature Review W. Węgrzyński & T. Lipecki https://doi.org/10.1007/s10694-018-0748-5
5. Self-organising swarms of firefighting drones: Harnessing the power of collective intelligence in decentralised multi-robot systems M. Innocente & P. Grasso https://doi.org/10.1016/j.jocs.2019.04.009
6. Interpolation framework to speed up near-surface wind simulations for data-driven wildfire applications O. Rios et al. https://doi.org/10.1071/WF17027
7. Learning-based prediction of wildfire spread with real-time rate of spread measurement C. Zhai et al. https://doi.org/10.1016/j.combustflame.2020.02.007
8. Improvement Forecasts for Asynchronous Observations in Data-Driven Wildfire Spread Prediction M. Zha et al. https://doi.org/10.1007/s10694-026-02007-w
9. Dynamic Risk Assessment of Wildfire-Induced Transmission Line Breakdown Based on Data Assimilation Method Z. Wang et al. https://doi.org/10.1007/s10694-025-01728-8
10. Summary of workshop large outdoor fires and the built environment S. Manzello et al. https://doi.org/10.1016/j.firesaf.2018.07.002
11. Forest Fire Spread Prediction and Assimilation Using the Deterministic Ensemble Kalman Filter T. Wu et al. https://doi.org/10.1007/s10694-024-01690-x
12. Modeling wildfires in the Alpine context: Lessons learnt from real case studies L. Malanchini et al. https://doi.org/10.1016/j.envc.2025.101357
13. Ensemble transform Kalman filter (ETKF) for large-scale wildland fire spread simulation using FARSITE tool and state estimation method T. Zhou et al. https://doi.org/10.1016/j.firesaf.2019.02.009
14. Wind and Fire Coupled Modelling—Part II: Good Practice Guidelines W. Węgrzyński et al. https://doi.org/10.1007/s10694-018-0749-4
15. Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data X. Liu et al. https://doi.org/10.3390/rs10101654
16. Automated location of active fire perimeters in aerial infrared imaging using unsupervised edge detectors M. Valero et al. https://doi.org/10.1071/WF17093
17. Combined estimation of fire perimeters and fuel adjustment factors in FARSITE for forecasting wildland fire propagation T. Zhou et al. https://doi.org/10.1016/j.firesaf.2020.103167
18. Breakthrough in the understanding of flaming wildfires G. Rein https://doi.org/10.1073/pnas.1512432112
19. A Data-Driven Fire Spread Simulator: Validation in Vall-llobrega's Fire O. Rios et al. https://doi.org/10.3389/fmech.2019.00008
20. Application cases of inverse modelling with the PROPTI framework L. Arnold et al. https://doi.org/10.1016/j.firesaf.2019.102835
21. Short-term fire front spread prediction using inverse modelling and airborne infrared images O. Rios et al. https://doi.org/10.1071/WF16031
22. A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires N. McCarthy et al. https://doi.org/10.3390/f12030294
23. Review and perspectives of digital twin systems for wildland fire management Y. Li et al. https://doi.org/10.1007/s11676-024-01810-x
24. Stochastic Approaches Systems to Predictive and Modeling Chilean Wildfires H. de la Fuente-Mella et al. https://doi.org/10.3390/math11204346
25. A Finsler geodesic spray paradigm for wildfire spread modelling S. Markvorsen https://doi.org/10.1016/j.nonrwa.2015.09.011