Articles | Volume 14, issue 6
Nat. Hazards Earth Syst. Sci., 14, 1491–1503, 2014
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: Numerical wildland combustion, from the flame to the...
Research article
13 Jun 2014
Research article
| 13 Jun 2014
Forecasting wind-driven wildfires using an inverse modelling approach
O. Rios et al.
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Cited
20 citations as recorded by crossref.
- Turbulence and fire-spotting effects into wild-land fire simulators I. Kaur et al. 10.1016/j.cnsns.2016.03.003
- A Data-Driven Fire Spread Simulator: Validation in Vall-llobrega's Fire O. Rios et al. 10.3389/fmech.2019.00008
- A Multi-observable Approach to Address the Ill-Posed Nature of Inverse Fire Modeling Problems M. Price et al. 10.1007/s10694-015-0541-7
- An integrated approach for tactical monitoring and data-driven spread forecasting of wildfires M. Valero et al. 10.1016/j.firesaf.2017.03.085
- Wind and Fire Coupled Modelling—Part I: Literature Review W. Węgrzyński & T. Lipecki 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 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. 10.1071/WF17027
- Learning-based prediction of wildfire spread with real-time rate of spread measurement C. Zhai et al. 10.1016/j.combustflame.2020.02.007
- Application cases of inverse modelling with the PROPTI framework L. Arnold et al. 10.1016/j.firesaf.2019.102835
- Short-term fire front spread prediction using inverse modelling and airborne infrared images O. Rios et al. 10.1071/WF16031
- Summary of workshop large outdoor fires and the built environment S. Manzello et al. 10.1016/j.firesaf.2018.07.002
- A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires N. McCarthy et al. 10.3390/f12030294
- Ensemble transform Kalman filter (ETKF) for large-scale wildland fire spread simulation using FARSITE tool and state estimation method T. Zhou et al. 10.1016/j.firesaf.2019.02.009
- Wind and Fire Coupled Modelling—Part II: Good Practice Guidelines W. Węgrzyński et al. 10.1007/s10694-018-0749-4
- Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data X. Liu et al. 10.3390/rs10101654
- Automated location of active fire perimeters in aerial infrared imaging using unsupervised edge detectors M. Valero et al. 10.1071/WF17093
- A Finsler geodesic spray paradigm for wildfire spread modelling S. Markvorsen 10.1016/j.nonrwa.2015.09.011
- Combined estimation of fire perimeters and fuel adjustment factors in FARSITE for forecasting wildland fire propagation T. Zhou et al. 10.1016/j.firesaf.2020.103167
- Breakthrough in the understanding of flaming wildfires G. Rein 10.1073/pnas.1512432112
- Wildland fire modeling with an Eulerian level set method and automated calibration C. Lautenberger 10.1016/j.firesaf.2013.08.014
19 citations as recorded by crossref.
- Turbulence and fire-spotting effects into wild-land fire simulators I. Kaur et al. 10.1016/j.cnsns.2016.03.003
- A Data-Driven Fire Spread Simulator: Validation in Vall-llobrega's Fire O. Rios et al. 10.3389/fmech.2019.00008
- A Multi-observable Approach to Address the Ill-Posed Nature of Inverse Fire Modeling Problems M. Price et al. 10.1007/s10694-015-0541-7
- An integrated approach for tactical monitoring and data-driven spread forecasting of wildfires M. Valero et al. 10.1016/j.firesaf.2017.03.085
- Wind and Fire Coupled Modelling—Part I: Literature Review W. Węgrzyński & T. Lipecki 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 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. 10.1071/WF17027
- Learning-based prediction of wildfire spread with real-time rate of spread measurement C. Zhai et al. 10.1016/j.combustflame.2020.02.007
- Application cases of inverse modelling with the PROPTI framework L. Arnold et al. 10.1016/j.firesaf.2019.102835
- Short-term fire front spread prediction using inverse modelling and airborne infrared images O. Rios et al. 10.1071/WF16031
- Summary of workshop large outdoor fires and the built environment S. Manzello et al. 10.1016/j.firesaf.2018.07.002
- A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires N. McCarthy et al. 10.3390/f12030294
- Ensemble transform Kalman filter (ETKF) for large-scale wildland fire spread simulation using FARSITE tool and state estimation method T. Zhou et al. 10.1016/j.firesaf.2019.02.009
- Wind and Fire Coupled Modelling—Part II: Good Practice Guidelines W. Węgrzyński et al. 10.1007/s10694-018-0749-4
- Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data X. Liu et al. 10.3390/rs10101654
- Automated location of active fire perimeters in aerial infrared imaging using unsupervised edge detectors M. Valero et al. 10.1071/WF17093
- A Finsler geodesic spray paradigm for wildfire spread modelling S. Markvorsen 10.1016/j.nonrwa.2015.09.011
- Combined estimation of fire perimeters and fuel adjustment factors in FARSITE for forecasting wildland fire propagation T. Zhou et al. 10.1016/j.firesaf.2020.103167
- Breakthrough in the understanding of flaming wildfires G. Rein 10.1073/pnas.1512432112
1 citations as recorded by crossref.
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