Articles | Volume 14, issue 6
https://doi.org/10.5194/nhess-14-1491-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/nhess-14-1491-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
13 Jun 2014
Research article |
| 13 Jun 2014
Forecasting wind-driven wildfires using an inverse modelling approach
O. Rios
Centre for Studies on Technological Risk (CERTEC), Universitat Politècnica de Catalunya, Av. Diagonal, 647, 08028 Barcelona, Spain
W. Jahn
Departamento de Ingeniería Mecánica y Metalúrgica, Pontificia Universidad Católica de Chile, Santiago, Chile
G. Rein
Department of Mechanical Engineering, Imperial College London, SW72AZ, London, UK
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Cited
24 citations as recorded by crossref.
- Turbulence and fire-spotting effects into wild-land fire simulators I. Kaur et al. https://doi.org/10.1016/j.cnsns.2016.03.003
- 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
- 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
- Wind and Fire Coupled Modelling—Part I: Literature Review W. Węgrzyński & T. Lipecki https://doi.org/10.1007/s10694-018-0748-5
- 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
- Interpolation framework to speed up near-surface wind simulations for data-driven wildfire applications O. Rios et al. https://doi.org/10.1071/WF17027
- 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
- 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
- Summary of workshop large outdoor fires and the built environment S. Manzello et al. https://doi.org/10.1016/j.firesaf.2018.07.002
- 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
- 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
- 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
- Wind and Fire Coupled Modelling—Part II: Good Practice Guidelines W. Węgrzyński et al. https://doi.org/10.1007/s10694-018-0749-4
- Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data X. Liu et al. https://doi.org/10.3390/rs10101654
- Automated location of active fire perimeters in aerial infrared imaging using unsupervised edge detectors M. Valero et al. https://doi.org/10.1071/WF17093
- 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
- Breakthrough in the understanding of flaming wildfires G. Rein https://doi.org/10.1073/pnas.1512432112
- A Data-Driven Fire Spread Simulator: Validation in Vall-llobrega's Fire O. Rios et al. https://doi.org/10.3389/fmech.2019.00008
- Application cases of inverse modelling with the PROPTI framework L. Arnold et al. https://doi.org/10.1016/j.firesaf.2019.102835
- Short-term fire front spread prediction using inverse modelling and airborne infrared images O. Rios et al. https://doi.org/10.1071/WF16031
- 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
- Review and perspectives of digital twin systems for wildland fire management Y. Li et al. https://doi.org/10.1007/s11676-024-01810-x
- Stochastic Approaches Systems to Predictive and Modeling Chilean Wildfires H. de la Fuente-Mella et al. https://doi.org/10.3390/math11204346
- A Finsler geodesic spray paradigm for wildfire spread modelling S. Markvorsen https://doi.org/10.1016/j.nonrwa.2015.09.011
24 citations as recorded by crossref.
- Turbulence and fire-spotting effects into wild-land fire simulators I. Kaur et al. https://doi.org/10.1016/j.cnsns.2016.03.003
- 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
- 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
- Wind and Fire Coupled Modelling—Part I: Literature Review W. Węgrzyński & T. Lipecki https://doi.org/10.1007/s10694-018-0748-5
- 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
- Interpolation framework to speed up near-surface wind simulations for data-driven wildfire applications O. Rios et al. https://doi.org/10.1071/WF17027
- 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
- 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
- Summary of workshop large outdoor fires and the built environment S. Manzello et al. https://doi.org/10.1016/j.firesaf.2018.07.002
- 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
- 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
- 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
- Wind and Fire Coupled Modelling—Part II: Good Practice Guidelines W. Węgrzyński et al. https://doi.org/10.1007/s10694-018-0749-4
- Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data X. Liu et al. https://doi.org/10.3390/rs10101654
- Automated location of active fire perimeters in aerial infrared imaging using unsupervised edge detectors M. Valero et al. https://doi.org/10.1071/WF17093
- 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
- Breakthrough in the understanding of flaming wildfires G. Rein https://doi.org/10.1073/pnas.1512432112
- A Data-Driven Fire Spread Simulator: Validation in Vall-llobrega's Fire O. Rios et al. https://doi.org/10.3389/fmech.2019.00008
- Application cases of inverse modelling with the PROPTI framework L. Arnold et al. https://doi.org/10.1016/j.firesaf.2019.102835
- Short-term fire front spread prediction using inverse modelling and airborne infrared images O. Rios et al. https://doi.org/10.1071/WF16031
- 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
- Review and perspectives of digital twin systems for wildland fire management Y. Li et al. https://doi.org/10.1007/s11676-024-01810-x
- Stochastic Approaches Systems to Predictive and Modeling Chilean Wildfires H. de la Fuente-Mella et al. https://doi.org/10.3390/math11204346
- A Finsler geodesic spray paradigm for wildfire spread modelling S. Markvorsen https://doi.org/10.1016/j.nonrwa.2015.09.011
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