Complex network statistics to the design of fire breaks for the control of fire spreading

A computational approach for identifying efficient fuel breaks partitions for the containment of fire incidents in forests is proposed. The approach is based on the complex networks statistics, namely the centrality measures and cellular automata modeling. The efficiency of various centrality statistics, such as betweenness, closeness, Bonacich and eigenvalue centrality to select fuel breaks partitions vs. the random-based distribution is demonstrated. Two examples of increasing complexity are considered: (a) an artificial forest of randomly distributed density of vegetation, and (b) a patch from the area of Vesuvio, National Park of Campania, Italy. Both cases assume flat terrain and single type of vegetation. Simulation results over an ensemble of lattice realizations and runs show that the proposed approach appears very promising as it produces statistically significant better outcomes when compared to the random distribution approach

Complex network theory criterion to distribute fuel breaks for the hazard control of fire spread in forests

We propose a computational methodology based on complex network theory for distributing fuel breaks to reduce the spread rate and size of wildland fires. The development of fire spread is modeled via a lattice network on the basis of a cellular automata model. We illustrate the proposed approach through a simplistic example which considers a square lattice with periodic boundary conditions with a single type of vegetation and varying density. Simulation results over an ensemble of lattice realizations show that the methodology outperforms significantly the benchmark tactic of random distribution of fuel breaks