Fire propagation visualization in real time
Our motivation comes from the need of a tailored computational tool for simulation and prediction of forest fire propagation, to be used by firefighters in Patagonia, Argentina. Based on previous works on Graphic Processing Units (GPU) for fitting and simulating fires in our region, we developed a visualization interface for real time computing, simulation and prediction of fire propagation. We have the possibility of changing the ensemble of raster maps layers to change the region in which fire will propagate.
The visualization platform runs on GPUs and the user can rotate and zoom the landscape to select the optimal view of fire propagation. Opacity of different layers can be regulated by the user, allowing to see fire propagation at the same time that underlying vegetation, wind direction and intensity.
The ignition point can also be selected by the user, and firebreaks can be plotted while simulation is going on.
After the performance of a high number of stochastic simulations in parallel in GPUs, the application shows a map of the final fire surface colored according to the probability that a given cell burns. In this way the user can visually identify the most critical direction for fire propagation, a useful information to stop fire optimizing resources, which is specially important when they are scarce like is the case of our Patagonia region.
J. H. Scott and R. E. Burgan, “Standard fire behavior fuel models: A comprehensive set for use with Rothermel’s surface fire spread model,” tech. rep., United States Department of Agriculture. Forest Service. Rocky Mountain. Research Station, 2005.
E. H. Anderson, “Aids to determining fuel models for estimating fire hehavior,” tech. rep., United States Department of Agriculture. Forest Service, 1982.
S. Taylor, R. G. Pike, andM. E. Alexander, Field Guide to the Canadian Forest Behaviour Prediction (FBP) System. Canadian Forest Service, 1996.
M. A. Finney, “Farsite: Fire area simulator - model development and evaluation,” tech. rep., USDA Forest Service, 2004.
R. V. Hoang,M. R. Sgambati, T. J. Brown, D. S. Coming, and F. C. Harris, “VFire: Immersive wildfire simulation and visualization,” Computers & Graphics, vol. 34, no. 6, pp. 655 – 664, 2010.
T. Ghisu, B. Arca, G. Pellizzaro, and P. Duce, “A level-set algorithm for simulating wildfire spread,” CMES Computer Modeling in Engineering and Sciences, vol. 102, no. 1, pp. 83 – 102, 2014.
T. Ghisu, B. Arca, G. Pellizzaro, and P. Duce, “An improved cellular automata for wildfire spread,” Procedia Computer Science, vol. 51, no. Supplement C, pp. 2287 – 2296, 2015. International Conference On Computational Science, ICCS 2015.
M. Denham and K. Laneri, “Using efficient parallelization in graphic processing units to parameterize stochastic fire propagationmodels,” Journal of Computational Science, vol. 25, pp. 76 – 88, 2018.
T.M. John Cheng,Max Grossman, Professional CUDA C Programming. Wrox, 2014.
S. Cook, CUDA Programming. A developer’s guide to parallel computing with GPUs. Morgan Kaufmann Publishers Inc., 2013.
D. B. Kirk and W.-m. W. Hwu, Programming Massively Parallel Processors: A Hands-on Approach. San Francisco, CA, USA:MorganKaufmann Publishers Inc., 1st ed., 2010.
J. M. Morales, M. Mermoz, J. H. Gowda, and T. Kitzberger, “A stochastic fire spread model for north patagonia based on fire occurrence maps,” Ecological Modelling, vol. 300, no. 0, pp. 73 – 80, 2015.
M. M´endez Garabetti, G. Bianchini, M. L. Tardivo, P. Caymes Scutari, and G. V. Gil Costa, “Hybrid-parallel uncertainty reduction method applied to forest fire spread prediction,” Journal of Computer Science and Technology, vol. 17, pp. p. 12–19, Apr. 2017.
M. Denham, “Dynamic data driven application for forest fire spread prediction,” Journal of Computer Science and Technology, vol. 12, pp. p. 84–86, Aug. 2012.
M. Denham, Predicci´on de la Evoluci´on de los Incendios Forestales Guiada Din´amicamente por los Datos. PhD thesis, Universidad Autónoma de Barcelona, 2009.
E. Meiri, “OGL. Modern OpenGL Tutorials.”
E. Luten, “OpenGL Book.”
G. Sanjuan, T. Margalef, and A. Cort´es, “Applying domain decomposition to wind field calculation,” Parallel Computing, vol. 57, pp. 185 –196, 2016.
U.S. Department of the Interior, “USGS: U.S. Geological Survey. Earth Explorer.”
CIEFAP-MAyDS, “Actualizaci ´on de la Clasificación de Tipos Forestales y Cobertura del Suelo de la Regi ´on Bosque Andino Patag´onico. Informe Final,” tech. rep., Centro de Investigaci´on y Extensi ´on Forestal Andino Patag´onico, 2016.
O. Cornut, “Immediate mode graphical user interface.”
Copyright (c) 2018 Monica Denham, Sigfrido Waidelich, Karina Laneri
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