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Based on complex network theory, we propose a computational methodology thataddresses the spatial distribution of fuel breaks for the inhibition of thespread and size of wildland fires on heterogeneous landscapes. This is atwo-tire approach where the dynamics of fire spread are modeled as a randomMarkov field process on a directed network whose edge weights, are provided bya state-of-the-art cellular automata model that integrates detailed GIS,landscape and meteorological data. Within this framework, the spatialdistribution of fuel breaks is reduced to the problem of finding the networknodes among which the fire spreads faster, thus their removal favours theinhibition of the fire propagation. Here this is accomplished exploiting theinformation centrality statistics. We illustrate the proposed approach through(a) an artificial forest of randomly distributed density of vegetation, and (b)a real-world case concerning the island of Rhodes in Greece whose a major partof its forest burned in 2008. Simulation results show that the methodologyoutperforms significantly the benchmark tactic of random distribution of fuelbreaks.

A complex network theory approach for the spatial distribution of fire breaks in heterogeneous forest landscapes for the control of wildland fires