Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models

TL;DR

This paper presents a GPU-accelerated cellular automata model for simulating stochastic fire propagation, incorporating landscape features and enabling estimation of ignition points and uncertainties.

Contribution

It introduces an efficient GPU-based implementation of a stochastic fire spread model that accounts for landscape factors and allows for ignition point estimation.

Findings

GPU implementation significantly speeds up fire simulation

Model accurately recovers fire propagation parameters

Can estimate ignition points and uncertainties

Abstract

Fire propagation is a major concern in the world in general and in Argentinian northwestern Patagonia in particular where every year hundreds of hectares are affected by both natural and anthropogenic forest fires. We developed an efficient cellular automata model in Graphic Processing Units (GPUs) to simulate fire propagation. The graphical advantages of GPUs were exploded by overlapping wind direction maps, as well as vegetation, slope and aspect maps, taking into account relevant landscape characteristics for fire propagation. Stochastic propagation was performed with a probability model that depends on aspect, slope, wind direction and vegetation type. Implementing a genetic algorithm search strategy we show, using simulated fires, that we recover the five parameter values that characterize fire propagation. The efficiency of the fire simulation procedure allowed us to also estimate the fire ignition point when it is unknown as well as its associated uncertainty, making this approach suitable for the analysis of fire spread based on maps of burned areas without knowing the point of origin of the fires or how they spread.