FlamMap is a fire analysis desktop application that runs in a 64-bit Windows Operating System environment. It can simulate potential fire behavior characteristics (spread rate, flame length, fireline intensity, etc.), fire growth and spread and conditional burn probabilities under constant environmental conditions (weather and fuel moisture). With the inclusion of FARSITE it can now compute wildfire growth and behavior for longer time periods under heterogeneous conditions of terrain, fuels, fuel moistures and weather.)

References in zbMATH (referenced in 29 articles )

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  1. Asensio, M. I.; Ferragut, L.; Álvarez, D.; Laiz, P.; Cascón, J. M.; Prieto, D.; Pagnini, G.: PhyFire: an online GIS-integrated wildfire spread simulation tool based on a semiphysical model (2021)
  2. Mikula, Karol; Urbán, Jozef; Kollár, Michal; Ambroz, Martin; Jarolímek, Ivan; Šibík, Jozef; Šibíková, Mária: An automated segmentation of NATURA 2000 habitats from sentinel-2 optical data (2021)
  3. Tapia, Tomás; Lorca, Álvaro; Olivares, Daniel; Negrete-Pincetic, Matías; Lamadrid L, Alberto J.: A robust decision-support method based on optimization and simulation for wildfire resilience in highly renewable power systems (2021)
  4. Zohdi, T. I.: A digital twin framework for machine learning optimization of aerial fire fighting and pilot safety (2021)
  5. Asensio-Sevilla, M. I.; Santos-Martín, M. T.; Álvarez-León, D.; Ferragut-Canals, L.: Global sensitivity analysis of fuel-type-dependent input variables of a simplified physical fire spread model (2020)
  6. Egorova, Vera N.; Trucchia, Andrea; Pagnini, Gianni: Fire-spotting generated fires. I: The role of atmospheric stability (2020)
  7. Grasso, Paolo; Innocente, Mauro S.: Physics-based model of wildfire propagation towards faster-than-real-time simulations (2020)
  8. Zohdi, T. I.: A machine-learning framework for rapid adaptive digital-twin based fire-propagation simulation in complex environments (2020)
  9. Ambroz, Martin; Balažovjech, Martin; Medl’a, Matej; Mikula, Karol: Numerical modeling of wildland surface fire propagation by evolving surface curves (2019)
  10. Bhuiyan, Tanveer Hossain; Moseley, Maxwell C.; Medal, Hugh R.; Rashidi, Eghbal; Grala, Robert K.: A stochastic programming model with endogenous uncertainty for incentivizing fuel reduction treatment under uncertain landowner behavior (2019)
  11. Trucchia, A.; Egorova, V.; Pagnini, G.; Rochoux, M. C.: 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 (2019)
  12. Rochoux, M. C.; Collin, A.; Zhang, C.; Trouvé, A.; Lucor, D.; Moireau, P.: Front shape similarity measure for shape-oriented sensitivity analysis and data assimilation for eikonal equation (2018)
  13. Ervilha, A. R.; Pereira, José M. C.; Pereira, J. C. F.: On the parametric uncertainty quantification of the Rothermel’s rate of spread model (2017)
  14. Rashidi, Eghbal; Medal, Hugh; Gordon, Jason; Grala, Robert; Varner, Morgan: A maximal covering location-based model for analyzing the vulnerability of landscapes to wildfires: assessing the worst-case scenario (2017)
  15. Kang, Ensil; Kim, Eun Heui; Lee, Jihoon: Traveling wave solutions for the combustion model of a shear flow in a cylinder (2016)
  16. Baranovskiy, N. V.: Algorithms for parallelizing a mathematical model of forest fires on supercomputers and theoretical estimates for the efficiency of parallel programs (2015)
  17. Duff, Thomas J.; Chong, Derek M.; Tolhurst, Kevin G.: Using discrete event simulation cellular automata models to determine multi-mode travel times and routes of terrestrial suppression resources to wildland fires (2015) ioport
  18. Hillen, T.; Greese, B.; Martin, J.; de Vries, G.: Birth-jump processes and application to forest fire spotting (2015)
  19. Barovik, D. V.; Korzyuk, V. I.; Taranchuk, V. B.: Methods of forest fires computer modelling (2013)
  20. Di Gregorio, Salvatore; Filippone, Giuseppe; Spataro, William; Trunfio, Giuseppe A.: Accelerating wildfire susceptibility mapping through GPGPU (2013) ioport

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