Forest fire spreading: a nonlinear stochastic model continuous in space and time

TL;DR

This paper introduces a continuous-space and time stochastic model for forest fire spreading that incorporates real data and environmental factors, providing a realistic and adaptable framework for understanding fire dynamics.

Contribution

It presents a novel stochastic model using integro-differential equations that accounts for various environmental factors and can incorporate real georeferenced data for improved accuracy.

Findings

Model captures fire spread influenced by wind, slope, and spotting.

Numerical simulations demonstrate fire crossing rivers due to spotting.

The model's solutions are proven to exist and be unique.

Abstract

Forest fire spreading is a complex phenomenon characterized by a stochastic behavior. Nowadays, the enormous quantity of georeferenced data and the availability of powerful techniques for their analysis can provide a very careful picture of forest fires opening the way to more realistic models. We propose a stochastic spreading model continuous in space and time that is able to use such data in their full power. The state of the forest fire is described by the subprobability densities of the green trees and of the trees on fire that can be estimated thanks to data coming from satellites and earth detectors. The fire dynamics is encoded into a density probability kernel which can take into account wind conditions, land slope, spotting phenomena and so on, bringing to a system of integro-differential equations for the probability densities. Existence and uniqueness of the solutions is proved by using Banach's fixed point theorem. The asymptotic behavior of the model is analyzed as well. Stochastic models based on cellular automata can be considered as particular cases of the present model from which they can be derived by space and/or time discretization. Suggesting a particular structure for the kernel, we obtain numerical simulations of the fire spreading under different conditions. For example, in the case of a forest fire evolving towards a river, the simulations show that the probability density of the trees on fire is different from zero beyond the river due to the spotting phenomenon. Firefighters interventions and weather changes can be easily introduced into the model.