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PROPAGATOR: An Operational Cellular-Automata Based Wildfire Simulator

Author(s): Andrea Trucchia, Mirko D'Andrea, Francesco Baghino, Paolo Fiorucci, Luca Ferraris, Dario Negro, Andrea Gollini, Massimiliano Severino
Year Published: 2020

PROPAGATOR is a stochastic cellular automaton model for forest fire spread simulation, conceived as a rapid method for fire risk assessment. The model uses high-resolution information such as topography and vegetation cover considering different types of vegetation. Input parameters are wind speed and direction and the ignition point. Dead fine fuel moisture content and firebreaks-fire fighting strategies can also be considered. The fire spread probability depends on vegetation type, slope, wind direction and speed, and fuel moisture content. The fire-propagation speed is determined through the adoption of a Rate of Spread model. PROPAGATOR simulates independent realizations of one stochastic fire propagation process, and at each time-step gives as output a map representing the probability of each cell of the domain to be affected by the fire. These probabilities are obtained computing the relative frequency of ignition of each cell. The model capabilities are assessed by reproducing a set of past Mediterranean fires occurred in different countries (Italy and Spain), using when available the real fire fighting patterns. PROPAGATOR simulated such scenarios with affordable computational resources and with short CPU-times. The outputs show a good agreement with the real burned areas, demonstrating that the PROPAGATOR can be useful for supporting decisions in Civil Protection and fire management activities.

Citation: Trucchia, Andrea; D'Andrea, Mirko; Baghino, Francesco; Fiorucci, Paolo; Ferraris, Luca; Negro, Dario; Gollini, Andrea; Severino, Massimiliano. 2020. PROPAGATOR: An operational cellular-automata based wildfire simulator. Fire 3(3):26. https://doi.org/10.3390/fire3030026
Topic(s): Fire Behavior, Simulation Modeling, Fuels
Ecosystem(s): None
Document Type: Book or Chapter or Journal Article
NRFSN number: 21627
FRAMES RCS number: 61600
Record updated: Aug 5, 2020