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
Viewed
Total article views: 3,615 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 09 May 2014)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,934 | 1,381 | 300 | 3,615 | 125 | 104 |
- HTML: 1,934
- PDF: 1,381
- XML: 300
- Total: 3,615
- BibTeX: 125
- EndNote: 104
Total article views: 2,829 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 10 Nov 2014)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,544 | 1,062 | 223 | 2,829 | 108 | 94 |
- HTML: 1,544
- PDF: 1,062
- XML: 223
- Total: 2,829
- BibTeX: 108
- EndNote: 94
Total article views: 786 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 09 May 2014)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 390 | 319 | 77 | 786 | 17 | 10 |
- HTML: 390
- PDF: 319
- XML: 77
- Total: 786
- BibTeX: 17
- EndNote: 10
Cited
56 citations as recorded by crossref.
- Mapping wildfire ignition probability and predictor sensitivity with ensemble-based machine learning Q. Tong & T. Gernay 10.1007/s11069-023-06172-x
- 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
- Observation data compression for variational assimilation of dynamical systems S. Cheng et al. 10.1016/j.jocs.2021.101405
- Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting S. Cheng et al. 10.1016/j.jcp.2022.111302
- Wildfire Spread Prediction and Assimilation for FARSITE Using Ensemble Kalman Filtering 1 T. Srivas et al. 10.1016/j.procs.2016.05.328
- State-parameter estimation approach for data-driven wildland fire spread modeling: Application to the 2012 RxCADRE S5 field-scale experiment C. Zhang et al. 10.1016/j.firesaf.2019.03.009
- Comparison of polynomial chaos and Gaussian process surrogates for uncertainty quantification and correlation estimation of spatially distributed open-channel steady flows P. Roy et al. 10.1007/s00477-017-1470-4
- Stochastic model reduction for polynomial chaos expansion of acoustic waves using proper orthogonal decomposition N. El Moçayd et al. 10.1016/j.ress.2019.106733
- Kalman Filter-Based Large-Scale Wildfire Monitoring With a System of UAVs Z. Lin et al. 10.1109/TIE.2018.2823658
- Wildland Fire Spread Modeling Using Convolutional Neural Networks J. Hodges & B. Lattimer 10.1007/s10694-019-00846-4
- Optimized Parametric Inference between Multilevel Physical Representations in Multigrid Ensemble Kalman Filter G. Moldovan et al. 10.2139/ssrn.4019071
- Intrusive and non-intrusive uncertainty quantification methodologies for pyrolysis modeling H. Jamil & F. Brännström 10.1016/j.firesaf.2023.104060
- Polynomial Surrogates for Open-Channel Flows in Random Steady State N. El Moçayd et al. 10.1007/s10666-017-9582-2
- Kriging-enhanced ensemble variational data assimilation for scalar-source identification in turbulent environments V. Mons et al. 10.1016/j.jcp.2019.07.054
- Developing a geospatial data-driven solution for rapid natural wildfire risk assessment B. Adhikari et al. 10.1016/j.apgeog.2020.102382
- Sparse grids‐based stochastic approximations with applications to aerodynamics sensitivity analysis A. Resmini et al. 10.1002/nme.5005
- Rapid prediction of wildfire spread using ensemble Kalman filter and polyline simplification S. Yoo & J. Song 10.1016/j.envsoft.2022.105610
- On the merits of sparse surrogates for global sensitivity analysis of multi-scale nonlinear problems: Application to turbulence and fire-spotting model in wildland fire simulators A. Trucchia et al. 10.1016/j.cnsns.2019.02.002
- 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
- Orthorectification of Helicopter-Borne High Resolution Experimental Burn Observation from Infra Red Handheld Imagers R. Paugam et al. 10.3390/rs13234913
- Accelerating inverse inference of ensemble Kalman filter via reduced-order model trained using adaptive sparse observations Y. Wang et al. 10.1016/j.jcp.2023.112600
- A Data-Driven Fire Spread Simulator: Validation in Vall-llobrega's Fire O. Rios et al. 10.3389/fmech.2019.00008
- Thermal Infrared Video Stabilization for Aerial Monitoring of Active Wildfires M. Valero et al. 10.1109/JSTARS.2021.3059054
- Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset X. Chen et al. 10.1109/ACCESS.2022.3222805
- 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
- Observation operators for assimilation of satellite observations in fluvial inundation forecasting E. Cooper et al. 10.5194/hess-23-2541-2019
- 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
- A multigrid/ensemble Kalman filter strategy for assimilation of unsteady flows G. Moldovan et al. 10.1016/j.jcp.2021.110481
- Unraveling the Complexity of Wildland Urban Interface Fires H. Mahmoud & A. Chulahwat 10.1038/s41598-018-27215-5
- Uncertainty quantification of forecast error in coupled fire–atmosphere wildfire spread simulations: sensitivity to the spatial resolution U. Ciri et al. 10.1071/WF20149
- Front shape similarity measure for data-driven simulations of wildland fire spread based on state estimation: Application to the RxCADRE field-scale experiment C. Zhang et al. 10.1016/j.proci.2018.07.112
- Optimized parametric inference for the inner loop of the Multigrid Ensemble Kalman Filter G. Moldovan et al. 10.1016/j.jcp.2022.111621
- 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
- Observation error covariance specification in dynamical systems for data assimilation using recurrent neural networks S. Cheng & M. Qiu 10.1007/s00521-021-06739-4
- Modelling wildland fire propagation by tracking random fronts G. Pagnini & A. Mentrelli 10.5194/nhess-14-2249-2014
55 citations as recorded by crossref.
- Mapping wildfire ignition probability and predictor sensitivity with ensemble-based machine learning Q. Tong & T. Gernay 10.1007/s11069-023-06172-x
- 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
- Observation data compression for variational assimilation of dynamical systems S. Cheng et al. 10.1016/j.jocs.2021.101405
- Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting S. Cheng et al. 10.1016/j.jcp.2022.111302
- Wildfire Spread Prediction and Assimilation for FARSITE Using Ensemble Kalman Filtering 1 T. Srivas et al. 10.1016/j.procs.2016.05.328
- State-parameter estimation approach for data-driven wildland fire spread modeling: Application to the 2012 RxCADRE S5 field-scale experiment C. Zhang et al. 10.1016/j.firesaf.2019.03.009
- Comparison of polynomial chaos and Gaussian process surrogates for uncertainty quantification and correlation estimation of spatially distributed open-channel steady flows P. Roy et al. 10.1007/s00477-017-1470-4
- Stochastic model reduction for polynomial chaos expansion of acoustic waves using proper orthogonal decomposition N. El Moçayd et al. 10.1016/j.ress.2019.106733
- Kalman Filter-Based Large-Scale Wildfire Monitoring With a System of UAVs Z. Lin et al. 10.1109/TIE.2018.2823658
- Wildland Fire Spread Modeling Using Convolutional Neural Networks J. Hodges & B. Lattimer 10.1007/s10694-019-00846-4
- Optimized Parametric Inference between Multilevel Physical Representations in Multigrid Ensemble Kalman Filter G. Moldovan et al. 10.2139/ssrn.4019071
- Intrusive and non-intrusive uncertainty quantification methodologies for pyrolysis modeling H. Jamil & F. Brännström 10.1016/j.firesaf.2023.104060
- Polynomial Surrogates for Open-Channel Flows in Random Steady State N. El Moçayd et al. 10.1007/s10666-017-9582-2
- Kriging-enhanced ensemble variational data assimilation for scalar-source identification in turbulent environments V. Mons et al. 10.1016/j.jcp.2019.07.054
- Developing a geospatial data-driven solution for rapid natural wildfire risk assessment B. Adhikari et al. 10.1016/j.apgeog.2020.102382
- Sparse grids‐based stochastic approximations with applications to aerodynamics sensitivity analysis A. Resmini et al. 10.1002/nme.5005
- Rapid prediction of wildfire spread using ensemble Kalman filter and polyline simplification S. Yoo & J. Song 10.1016/j.envsoft.2022.105610
- On the merits of sparse surrogates for global sensitivity analysis of multi-scale nonlinear problems: Application to turbulence and fire-spotting model in wildland fire simulators A. Trucchia et al. 10.1016/j.cnsns.2019.02.002
- 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
- Orthorectification of Helicopter-Borne High Resolution Experimental Burn Observation from Infra Red Handheld Imagers R. Paugam et al. 10.3390/rs13234913
- Accelerating inverse inference of ensemble Kalman filter via reduced-order model trained using adaptive sparse observations Y. Wang et al. 10.1016/j.jcp.2023.112600
- A Data-Driven Fire Spread Simulator: Validation in Vall-llobrega's Fire O. Rios et al. 10.3389/fmech.2019.00008
- Thermal Infrared Video Stabilization for Aerial Monitoring of Active Wildfires M. Valero et al. 10.1109/JSTARS.2021.3059054
- Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset X. Chen et al. 10.1109/ACCESS.2022.3222805
- 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
- Observation operators for assimilation of satellite observations in fluvial inundation forecasting E. Cooper et al. 10.5194/hess-23-2541-2019
- 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
- A multigrid/ensemble Kalman filter strategy for assimilation of unsteady flows G. Moldovan et al. 10.1016/j.jcp.2021.110481
- Unraveling the Complexity of Wildland Urban Interface Fires H. Mahmoud & A. Chulahwat 10.1038/s41598-018-27215-5
- Uncertainty quantification of forecast error in coupled fire–atmosphere wildfire spread simulations: sensitivity to the spatial resolution U. Ciri et al. 10.1071/WF20149
- Front shape similarity measure for data-driven simulations of wildland fire spread based on state estimation: Application to the RxCADRE field-scale experiment C. Zhang et al. 10.1016/j.proci.2018.07.112
- Optimized parametric inference for the inner loop of the Multigrid Ensemble Kalman Filter G. Moldovan et al. 10.1016/j.jcp.2022.111621
- 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
- Observation error covariance specification in dynamical systems for data assimilation using recurrent neural networks S. Cheng & M. Qiu 10.1007/s00521-021-06739-4
1 citations as recorded by crossref.
Saved (final revised paper)
Saved (preprint)
Latest update: 20 Apr 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...
Special issue
Altmetrics
Final-revised paper
Preprint