Primary Faculty Mentor Name

Chris Danforth

Status

Undergraduate

Student College

College of Engineering and Mathematical Sciences

Program/Major

Complex Systems

Primary Research Category

Engineering & Physical Sciences

Secondary Research Category

Food & Environment Studies

Tertiary Research Category

Biological Sciences

Presentation Title

An Agent Based Model for Suppression of Wildfire Contagion

Time

All day

Location

Humanities Center Creative Lounge

Abstract

As wildfires threaten Northwestern America, it becomes increasingly important to contain a fire soon after its ignition. Given a fire’s origin, the environment it propagates over, and a constrained amount of resources, we propose a framework for analyzing different containment strategies that aim to minimize wildfire impact. Inspired by probabilistic cellular automata models including Highly Optimized Tolerance, we simulate fire propagation according to biased stochastic probability functions on a 2 dimensional, hexagonal lattice. Rational artificial agents are tasked with building fire breaks to contain the spread in real time. Given only spatial context, agents are allowed to act on sites in their immediate neighborhood. By introducing different tuneable parameters such as topography, fire strength, and resource scarcity, we compare different containment strategies. Our goal is for simulations in this framework to inform fire fighting efforts in near real time.

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An Agent Based Model for Suppression of Wildfire Contagion

As wildfires threaten Northwestern America, it becomes increasingly important to contain a fire soon after its ignition. Given a fire’s origin, the environment it propagates over, and a constrained amount of resources, we propose a framework for analyzing different containment strategies that aim to minimize wildfire impact. Inspired by probabilistic cellular automata models including Highly Optimized Tolerance, we simulate fire propagation according to biased stochastic probability functions on a 2 dimensional, hexagonal lattice. Rational artificial agents are tasked with building fire breaks to contain the spread in real time. Given only spatial context, agents are allowed to act on sites in their immediate neighborhood. By introducing different tuneable parameters such as topography, fire strength, and resource scarcity, we compare different containment strategies. Our goal is for simulations in this framework to inform fire fighting efforts in near real time.