Articles | Volume 14, issue 11
https://doi.org/10.5194/nhess-14-2951-2014
© 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-14-2951-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
10 Nov 2014
Research article |
| 10 Nov 2014
Towards predictive data-driven simulations of wildfire spread – Part I: Reduced-cost Ensemble Kalman Filter based on a Polynomial Chaos surrogate model for parameter estimation
M. C. Rochoux
CERFACS, 42 avenue Gaspard Coriolis, 31057 Toulouse Cedex 01, France
SUC/CNRS-URA1875, 42 avenue Gaspard Coriolis, 31057 Toulouse CEDEX 01, France
Ecole Centrale Paris, Grande voie des vignes, 92295 Châtenay-Malabry, France
EM2C/CNRS-UPR288, Grande voie des vignes, 92295 Châtenay-Malabry, France
S. Ricci
CERFACS, 42 avenue Gaspard Coriolis, 31057 Toulouse Cedex 01, France
SUC/CNRS-URA1875, 42 avenue Gaspard Coriolis, 31057 Toulouse CEDEX 01, France
D. Lucor
Institut d'Alembert, Université Pierre et Marie Curie, CNRS-UMR7190, 4 place Jussieu, 75006 Paris, France
B. Cuenot
CERFACS, 42 avenue Gaspard Coriolis, 31057 Toulouse Cedex 01, France
A. Trouvé
Dept. of Fire Protection Engineering, University of Maryland, College Park, MD 20742, USA
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59 citations as recorded by crossref.
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- Towards predictive data-driven simulations of wildfire spread – Part II: Ensemble Kalman Filter for the state estimation of a front-tracking simulator of wildfire spread M. Rochoux et al. 10.5194/nhess-15-1721-2015
- Inferring energy incident on sensors in low-intensity surface fires from remotely sensed radiation and using it to predict tree stem injury M. Dickinson et al. 10.1071/WF18164
- A reduced order model based on Kalman filtering for sequential data assimilation of turbulent flows M. Meldi & A. Poux 10.1016/j.jcp.2017.06.042
- Feasibility of DEIM for retrieving the initial field via dimensionality reduction Y. Wang et al. 10.1016/j.jcp.2020.110005
- Emulation of wildland fire spread simulation using deep learning F. Allaire et al. 10.1016/j.neunet.2021.04.006
- Experimental study of krone burning parameters of the most common trees in Vietnam S. Puzach & L. Tuan 10.18322/PVB.2019.28.06.10-17
- Application of the EnKF method for real-time forecasting of smoke movement during tunnel fires J. Ji et al. 10.1016/j.advengsoft.2017.10.007
- Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review S. Cheng et al. 10.1109/JAS.2023.123537
- Evaluation of a data-driven wildland fire spread forecast model with spatially-distributed parameter estimation in simulations of the FireFlux I field-scale experiment C. Zhang et al. 10.1016/j.firesaf.2017.03.057
- 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
- 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
- Augmented state estimation of urban settings using on-the-fly sequential Data Assimilation L. Villanueva et al. 10.1016/j.compfluid.2023.106118
- An integrated approach for tactical monitoring and data-driven spread forecasting of wildfires M. Valero et al. 10.1016/j.firesaf.2017.03.085
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- Developing a geospatial data-driven solution for rapid natural wildfire risk assessment B. Adhikari et al. 10.1016/j.apgeog.2020.102382
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- Short-term fire front spread prediction using inverse modelling and airborne infrared images O. Rios et al. 10.1071/WF16031
- Data Assimilation of Wildfires with Fuel Adjustment Factors in farsite using Ensemble Kalman Filtering * *This work is funded by NSF 1331615 under CI, Information Technology Research and SEES Hazards programs T. Srivas et al. 10.1016/j.procs.2017.05.197
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- Synchronization and optimization of Large Eddy Simulation using an online Ensemble Kalman Filter L. Villanueva et al. 10.1016/j.ijheatfluidflow.2024.109597
- Topological data assimilation using Wasserstein distance L. Li et al. 10.1088/1361-6420/aae993
- Parameter estimation of fire propagation models using level set methods A. Alessandri et al. 10.1016/j.apm.2020.11.030
- Multigrid sequential data assimilation for the Large Eddy Simulation of a massively separated bluff-body flow G. Moldovan et al. 10.1016/j.compfluid.2024.106385
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- Uncertainty quantification of forecast error in coupled fire–atmosphere wildfire spread simulations: sensitivity to the spatial resolution U. Ciri et al. 10.1071/WF20149
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- An Accurate Fire‐Spread Algorithm in the Weather Research and Forecasting Model Using the Level‐Set Method D. Muñoz‐Esparza et al. 10.1002/2017MS001108
- Towards an Integrated Cyberinfrastructure for Scalable Data-driven Monitoring, Dynamic Prediction and Resilience of Wildfires I. Altintas et al. 10.1016/j.procs.2015.05.296
- Automated location of active fire perimeters in aerial infrared imaging using unsupervised edge detectors M. Valero et al. 10.1071/WF17093
- Selection justification of the wood pulp and crown combustion parameters for the calculation of the crown forest fires impact on Vietnamese energy facilities L. Tuan et al. 10.1051/e3sconf/202342004022
- GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography W. Li & C. Hsu 10.3390/ijgi11070385
- Markov Chain Monte Carlo (MCMC) uncertainty analysis for watershed water quality modeling and management Y. Zheng & F. Han 10.1007/s00477-015-1091-8
- Subgrid-scale fire front reconstruction for ensemble coupled atmosphere-fire simulations of the FireFlux I experiment A. Costes et al. 10.1016/j.firesaf.2021.103475
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Latest update: 13 Nov 2024
Short summary
This paper presents a data-driven wildfire simulator for forecasting wildfire spread scenarios at a reduced computational cost that is consistent with operational systems. A wildfire spread simulator combined with an ensemble-based data assimilation algorithm is indeed a promising approach to reduce uncertainties in the forecast location of the fire front and to introduce a paradigm shift in the wildfire emergency response.
This paper presents a data-driven wildfire simulator for forecasting wildfire spread scenarios...
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