CHEMICAL ENGINEERING TRANSACTIONS VOL. 36, 2014 A publication of The Italian Association of Chemical Engineering www.aidic.it/cet Guest Editors: Valerio Cozzani, Eddy de Rademaeker Copyright © 2014, AIDIC Servizi S.r.l., I SBN 978-88-95608-27-3; I SSN 2283-9216 Detecting Weak Points of Wildland Fire Spread: A Cellular Automata Model Risk Assessment Simulation Approach Lucia Russo a , Paola Russo b , Dimitris Vakalis c ,Constantinos Siettos* d a Istituto di Ricerche sulla Combustione, Consiglio Nazionale delle Ricerche,80125, Napoli, Italia b Department of Chemical Engineering Materials Environment, Sapienza University of Rome, Rome, Italy c Department of Forest Resources Development, Ministry of Rural Development and Food, Athens, Greece d School of Applied Mathematics and Physical Sciences, National Technical University of Athens, Athens,Greece ksiet@mail.ntua.gr In this work, we propose a risk-assessment approach based on Cellular Automata (CA) simulations which incorporate both theoretical/ first principles and (semi)empirical fire behavioural models. The proposed approach can deal with spatial heterogeneity in both the fuel and landscape characteristics, can be coupled with Geographical Information Systems (GIS) and can take as input local meteorological data (even in real time). Using the CA model we are able to construct the topographic map of hazard intensity defined in terms of the expected burned area resulting from an ignition in a particular point of the region under surveillance. For our illustrations we used the case of Spetses Island whose a major part of its forest burned in 1990. Using the proposed framework, we revealed the weak points of fire spread risk, i.e. potential ignitions points which result to maximum likelihood of burned area. 1. Introduction One of the most challenging and important problems in ecology is the design and implementation of efficient wildland fire-prevention and fire-suppression policies in forests (Albini and Brown, 1996). Wildland fires have caused numerous irreversible environmental damages with serious negative ecological and socio-economic implications such as the loss of human lives, flora and fauna bio-diversity and rare-species extinction, habitant fragmentation, floods, loss of timber harvest capability, economic loses etc. There is no doubt that the systematic -in terms of mathematical modelling and analysis – quantification of the fire spread dynamics is of outmost importance toward the risk assessment of a potential outbreak. Factors such as weather/climate conditions (wind field, air humidity and temperature), characteristics of the distributed local fuel (type and structure of the vegetation, moisture and density), landscape/earth characteristics (slope, fragmentation and natural barriers) as well as fire-suppression tactics are key elements toward this effort (Bergeron and Flannigan, 1995). However, due to the inherent complexity of such a phenomenon, deploying at different time and space scales risk assessment is far from simple, yet a most challenging one. Traditionally, wildfire risk assessment is evaluated considering just the fuel content in the area under study. Thus all the factors mentioned above, which interact nonlinearly to determine the dynamics of the spreading of forest wildfires, are not considered in the evaluation of the hazard level. Recently, some studies have considered the adoption of software for the simulation of the wildfire spreading for the construction of hazard map (Carmel et al., 2009; Ager and Finney, 2009; Ager et al., 2010). In Carmel et al. (2009), a high number of simulations have been carried out to compute the burn probability and fire intensity map, changing the weather condition and the ignition point. The simulation approach is used in Ager and Finney (2009) and Ager et al. (2010) to construct risk maps when different strategies of risk reduction are adopted. Clearly, the core of the approach is the adoption of accurate and efficient simulation models. The most known and used fire model is the Rothermel fire model (Rothermel, 1972), which gives the rate and direction of fire spreading as function of the local landscape and weather conditions. Rothermel’s equations have subsequently been applied in a variety of approaches which in terms of spatial representation can be categorized in two types (Sullivan, 2009). The first type consists of DOI: 10.3303/CET1436043 Please cite this article as: Russo L., Russo P., Vakalis D., Siettos C., 2014, Detecting weak points of wildland fire spread: a cellular atomata model risk assesemnt simulation approach, Chemical Engineering Transactions, 36, 253-258 DOI: 10.3303/CET1436043 253