72 citations as recorded by crossref.

1. Mapping wildfire ignition probability and predictor sensitivity with ensemble-based machine learning Q. Tong & T. Gernay https://doi.org/10.1007/s11069-023-06172-x
2. Toward autonomous wildfire management: Advances in multimodal sensing, AI detection, sensor fusion, estimation and decision support review T. Kairouz & H. Hashim https://doi.org/10.1016/j.rineng.2026.112218
3. 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. https://doi.org/10.5194/nhess-15-1721-2015
4. 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. https://doi.org/10.1071/WF18164
5. A reduced order model based on Kalman filtering for sequential data assimilation of turbulent flows M. Meldi & A. Poux https://doi.org/10.1016/j.jcp.2017.06.042
6. Feasibility of DEIM for retrieving the initial field via dimensionality reduction Y. Wang et al. https://doi.org/10.1016/j.jcp.2020.110005
7. Emulation of wildland fire spread simulation using deep learning F. Allaire et al. https://doi.org/10.1016/j.neunet.2021.04.006
8. Experimental study of krone burning parameters of the most common trees in Vietnam S. Puzach & L. Tuan https://doi.org/10.18322/PVB.2019.28.06.10-17
9. Application of the EnKF method for real-time forecasting of smoke movement during tunnel fires J. Ji et al. https://doi.org/10.1016/j.advengsoft.2017.10.007
10. Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review S. Cheng et al. https://doi.org/10.1109/JAS.2023.123537
11. 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. https://doi.org/10.1016/j.firesaf.2017.03.057
12. 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
13. 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
14. 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
15. Augmented state estimation of urban settings using on-the-fly sequential Data Assimilation L. Villanueva et al. https://doi.org/10.1016/j.compfluid.2023.106118
16. 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
17. Observation data compression for variational assimilation of dynamical systems S. Cheng et al. https://doi.org/10.1016/j.jocs.2021.101405
18. Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting S. Cheng et al. https://doi.org/10.1016/j.jcp.2022.111302
19. Wildfire Spread Prediction and Assimilation for FARSITE Using Ensemble Kalman Filtering 1 T. Srivas et al. https://doi.org/10.1016/j.procs.2016.05.328
20. State-parameter estimation approach for data-driven wildland fire spread modeling: Application to the 2012 RxCADRE S5 field-scale experiment C. Zhang et al. https://doi.org/10.1016/j.firesaf.2019.03.009
21. 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. https://doi.org/10.1007/s00477-017-1470-4
22. Stochastic model reduction for polynomial chaos expansion of acoustic waves using proper orthogonal decomposition N. El Moçayd et al. https://doi.org/10.1016/j.ress.2019.106733
23. Surrogate-based ensemble data assimilation for reducing uncertainty in large-eddy simulation of microscale pollutant dispersion E. Lumet et al. https://doi.org/10.1016/j.buildenv.2025.113863
24. Mapping of Fire Perimeter and Reconstruction of Prescribed Grassfire Evolution Using Thermal Imagery From a Low-Altitude UAS Z. Lin et al. https://doi.org/10.1109/JSTARS.2026.3676906
25. Kalman Filter-Based Large-Scale Wildfire Monitoring With a System of UAVs Z. Lin et al. https://doi.org/10.1109/TIE.2018.2823658
26. Wildland Fire Spread Modeling Using Convolutional Neural Networks J. Hodges & B. Lattimer https://doi.org/10.1007/s10694-019-00846-4
27. Optimized Parametric Inference between Multilevel Physical Representations in Multigrid Ensemble Kalman Filter G. Moldovan et al. https://doi.org/10.2139/ssrn.4019071
28. Intrusive and non-intrusive uncertainty quantification methodologies for pyrolysis modeling H. Jamil & F. Brännström https://doi.org/10.1016/j.firesaf.2023.104060
29. Polynomial Surrogates for Open-Channel Flows in Random Steady State N. El Moçayd et al. https://doi.org/10.1007/s10666-017-9582-2
30. Kriging-enhanced ensemble variational data assimilation for scalar-source identification in turbulent environments V. Mons et al. https://doi.org/10.1016/j.jcp.2019.07.054
31. An intercomparison of wildfire spread models in Fire Dynamics Simulator (FDS) using FireFlux II experiment Y. Kim et al. https://doi.org/10.1016/j.firesaf.2026.104675
32. Developing a geospatial data-driven solution for rapid natural wildfire risk assessment B. Adhikari et al. https://doi.org/10.1016/j.apgeog.2020.102382
33. Sparse grids‐based stochastic approximations with applications to aerodynamics sensitivity analysis A. Resmini et al. https://doi.org/10.1002/nme.5005
34. Rapid prediction of wildfire spread using ensemble Kalman filter and polyline simplification S. Yoo & J. Song https://doi.org/10.1016/j.envsoft.2022.105610
35. 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. https://doi.org/10.1016/j.cnsns.2019.02.002
36. 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
37. Orthorectification of Helicopter-Borne High Resolution Experimental Burn Observation from Infra Red Handheld Imagers R. Paugam et al. https://doi.org/10.3390/rs13234913
38. Accelerating inverse inference of ensemble Kalman filter via reduced-order model trained using adaptive sparse observations Y. Wang et al. https://doi.org/10.1016/j.jcp.2023.112600
39. The distributed strategy for asynchronous observations in data-driven wildland fire spread prediction M. Zha et al. https://doi.org/10.1071/WF23165
40. A Data-Driven Fire Spread Simulator: Validation in Vall-llobrega's Fire O. Rios et al. https://doi.org/10.3389/fmech.2019.00008
41. Thermal Infrared Video Stabilization for Aerial Monitoring of Active Wildfires M. Valero et al. https://doi.org/10.1109/JSTARS.2021.3059054
42. Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset X. Chen et al. https://doi.org/10.1109/ACCESS.2022.3222805
43. Short-term fire front spread prediction using inverse modelling and airborne infrared images O. Rios et al. https://doi.org/10.1071/WF16031
44. 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. https://doi.org/10.1016/j.procs.2017.05.197
45. Observation operators for assimilation of satellite observations in fluvial inundation forecasting E. Cooper et al. https://doi.org/10.5194/hess-23-2541-2019
46. Synchronization and optimization of Large Eddy Simulation using an online Ensemble Kalman Filter L. Villanueva et al. https://doi.org/10.1016/j.ijheatfluidflow.2024.109597
47. Topological data assimilation using Wasserstein distance L. Li et al. https://doi.org/10.1088/1361-6420/aae993
48. Parameter estimation of fire propagation models using level set methods A. Alessandri et al. https://doi.org/10.1016/j.apm.2020.11.030
49. Multigrid sequential data assimilation for the Large Eddy Simulation of a massively separated bluff-body flow G. Moldovan et al. https://doi.org/10.1016/j.compfluid.2024.106385
50. A multigrid/ensemble Kalman filter strategy for assimilation of unsteady flows G. Moldovan et al. https://doi.org/10.1016/j.jcp.2021.110481
51. Unraveling the Complexity of Wildland Urban Interface Fires H. Mahmoud & A. Chulahwat https://doi.org/10.1038/s41598-018-27215-5
52. 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
53. Selection algorithm for observation points in environmental data assimilation based on the quantum squeezing effect H. Yang et al. https://doi.org/10.1007/s11433-025-2703-7
54. Uncertainty quantification of forecast error in coupled fire–atmosphere wildfire spread simulations: sensitivity to the spatial resolution U. Ciri et al. https://doi.org/10.1071/WF20149
55. Physics-informed neural networks for parameter learning of wildfire spreading K. Vogiatzoglou et al. https://doi.org/10.1016/j.cma.2024.117545
56. 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. https://doi.org/10.1016/j.proci.2018.07.112
57. A distributed data-driven system for dynamic wildfire monitoring and forecasting M. Zha et al. https://doi.org/10.1016/j.envsoft.2026.106854
58. Optimized parametric inference for the inner loop of the Multigrid Ensemble Kalman Filter G. Moldovan et al. https://doi.org/10.1016/j.jcp.2022.111621
59. Comparison of ensemble assimilation methods in a hydrological model dedicated to agricultural best management practices E. Rouzies et al. https://doi.org/10.5194/hess-30-1-2026
60. An Accurate Fire‐Spread Algorithm in the Weather Research and Forecasting Model Using the Level‐Set Method D. Muñoz‐Esparza et al. https://doi.org/10.1002/2017MS001108
61. Towards an Integrated Cyberinfrastructure for Scalable Data-driven Monitoring, Dynamic Prediction and Resilience of Wildfires I. Altintas et al. https://doi.org/10.1016/j.procs.2015.05.296
62. Automated location of active fire perimeters in aerial infrared imaging using unsupervised edge detectors M. Valero et al. https://doi.org/10.1071/WF17093
63. RETRACTED: 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. https://doi.org/10.1051/e3sconf/202342004022
64. Reproduction of Smaller Wildfire Perimeters Observed by Polar-Orbiting Satellites Using ROS Adjustment Factors and Wildfire Spread Simulators S. Yoo et al. https://doi.org/10.3390/rs17162824
65. Ensemble-Based Data Assimilation Without CFD-Generated Ensembles: A Scaling Approach for RANS Simulations Under ABL Inflow N. Pallas & D. Bouris https://doi.org/10.1007/s10494-026-00761-z
66. GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography W. Li & C. Hsu https://doi.org/10.3390/ijgi11070385
67. Markov Chain Monte Carlo (MCMC) uncertainty analysis for watershed water quality modeling and management Y. Zheng & F. Han https://doi.org/10.1007/s00477-015-1091-8
68. Subgrid-scale fire front reconstruction for ensemble coupled atmosphere-fire simulations of the FireFlux I experiment A. Costes et al. https://doi.org/10.1016/j.firesaf.2021.103475
69. Enhancement of large-eddy simulations for the prediction of an intake flow rig using sequential data assimilation L. Villanueva et al. https://doi.org/10.1017/jfm.2025.10863
70. A Novel Divergence-Based Approach for Fire Ignition Point Localization Through Smoke Pattern Analysis S. Lim & J. Kim https://doi.org/10.1109/ACCESS.2025.3648457
71. Dynamic correction of forest fire spread prediction using observation error covariance matrix estimation technique based on FLC-GRU T. Wu et al. https://doi.org/10.1186/s42408-024-00329-0
72. Observation error covariance specification in dynamical systems for data assimilation using recurrent neural networks S. Cheng & M. Qiu https://doi.org/10.1007/s00521-021-06739-4