FARSITE

FARSITE computes wildfire growth and behavior for long time periods under heterogeneous conditions of terrain, fuels, and weather. FARSITE is a fire growth simulation modeling system. It uses spatial information on topography and fuels along with weather and wind files. It incorporates existing models for surface fire, crown fire, spotting, post-frontal combustion, and fire acceleration into a 2-dimensional fire growth model. FARSITE is widely used by the U.S. Forest Service, National Park Service, and other federal and state land management agencies to simulate the spread of wildfires and fire use for resource benefit across the landscape. It is designed for users familiar with fuels, weather, topography, wildfire situations and the associated terminology. Because of its complexity, only users with the proper fire behavior training and experience should use FARSITE where the outputs are to be used for making fire and land management decisions. ** FARSITE4 will no longer be supported or available for download. FlamMap6 now includes FARSITE. **


References in zbMATH (referenced in 30 articles )

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  1. Cheng, Sibo; Prentice, I. Colin; Huang, Yuhan; Jin, Yufang; Guo, Yi-Ke; Arcucci, Rossella: Data-driven surrogate model with latent data assimilation: application to wildfire forecasting (2022)
  2. Paige, John; Fuglstad, Geir-Arne; Riebler, Andrea; Wakefield, Jon: Bayesian multiresolution modeling of georeferenced data: an extension of `LatticeKrig’ (2022)
  3. 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)
  4. 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)
  5. Pais, Cristobal; Carrasco, Jaime; Elimbi Moudio, Pelagie; Shen, Zuo-Jun Max: Downstream protection value: detecting critical zones for effective fuel-treatment under wildfire risk (2021)
  6. 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)
  7. Zohdi, T. I.: A digital twin framework for machine learning optimization of aerial fire fighting and pilot safety (2021)
  8. Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
  9. 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)
  10. Egorova, Vera N.; Trucchia, Andrea; Pagnini, Gianni: Fire-spotting generated fires. I: The role of atmospheric stability (2020)
  11. Grasso, Paolo; Innocente, Mauro S.: Physics-based model of wildfire propagation towards faster-than-real-time simulations (2020)
  12. Zohdi, T. I.: A machine-learning framework for rapid adaptive digital-twin based fire-propagation simulation in complex environments (2020)
  13. Ambroz, Martin; Balažovjech, Martin; Medl’a, Matej; Mikula, Karol: Numerical modeling of wildland surface fire propagation by evolving surface curves (2019)
  14. Finley, Andrew O.; Datta, Abhirup; Cook, Bruce D.; Morton, Douglas C.; Andersen, Hans E.; Banerjee, Sudipto: Efficient algorithms for Bayesian nearest neighbor Gaussian processes (2019)
  15. 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)
  16. 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)
  17. Kang, Ensil; Kim, Eun Heui; Lee, Jihoon: Traveling wave solutions for the combustion model of a shear flow in a cylinder (2016)
  18. Baranovskiy, N. V.: Algorithms for parallelizing a mathematical model of forest fires on supercomputers and theoretical estimates for the efficiency of parallel programs (2015)
  19. 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
  20. Hillen, T.; Greese, B.; Martin, J.; de Vries, G.: Birth-jump processes and application to forest fire spotting (2015)

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