Document Type
Event
Start Date
1-12-2022 5:00 PM
Description
Discrete computational models known as cellular automata (CA) utilize discrete spatial cells, each existing in one of a set of possible states at any given moment. Transition rules specify how a given cell’s state evolves in subsequent time steps and is dependent on the states of the given cell’s neighborhood of surrounding cells. A cellular automaton model can also account for the influence of other external or physical factors on the evolution of a given cell’s state. These capabilities afforded by CA models make it an ideal tool to simulate the propagation of wildfires. One specific study carried out by Freire et al. [2019] sought to improve upon the benchmark concept by attempting to incorporating topographical, meteorological, and fuel loading factors into the transition rules of their probabilistic CA model. This project proposes two ways to improve the accuracy of their proposed CA to model wildfire propagation to reduce uncertainty intrinsic to a probabilistic approach. First, we attempt to incorporate fire weather indices that account for relevant meteorological, climatological, and fuel stress and flammability conditions that affect the ability of, and therefore the probability that, fuel within a cell can ignite within a given time step. Next, Freire et al. [2019] determined a constant burn rate to use for each time step based on empirical and probabilistic data that is specific to a certain fire they studied. The capability to account for burn rate for cells with varying fuel load factors will also be assessed in this paper. Ultimately, the purpose of these efforts is to increase the accuracy of fire propagation simulations using cellular automata models to assist emergency management and firefighters warn, evacuate, fight, and allocate resources to efficiently protect life and property.
Included in
GR-306 Improving Wildfire Propagation Simulations Using Cellular Automata to Help Emergency Management
Discrete computational models known as cellular automata (CA) utilize discrete spatial cells, each existing in one of a set of possible states at any given moment. Transition rules specify how a given cell’s state evolves in subsequent time steps and is dependent on the states of the given cell’s neighborhood of surrounding cells. A cellular automaton model can also account for the influence of other external or physical factors on the evolution of a given cell’s state. These capabilities afforded by CA models make it an ideal tool to simulate the propagation of wildfires. One specific study carried out by Freire et al. [2019] sought to improve upon the benchmark concept by attempting to incorporating topographical, meteorological, and fuel loading factors into the transition rules of their probabilistic CA model. This project proposes two ways to improve the accuracy of their proposed CA to model wildfire propagation to reduce uncertainty intrinsic to a probabilistic approach. First, we attempt to incorporate fire weather indices that account for relevant meteorological, climatological, and fuel stress and flammability conditions that affect the ability of, and therefore the probability that, fuel within a cell can ignite within a given time step. Next, Freire et al. [2019] determined a constant burn rate to use for each time step based on empirical and probabilistic data that is specific to a certain fire they studied. The capability to account for burn rate for cells with varying fuel load factors will also be assessed in this paper. Ultimately, the purpose of these efforts is to increase the accuracy of fire propagation simulations using cellular automata models to assist emergency management and firefighters warn, evacuate, fight, and allocate resources to efficiently protect life and property.