Probabilistic prediction of wildfire economic losses to housing in Cyprus using Bayesian network analysis
P. Papakosta A C , G. Xanthopoulos B and D. Straub A
+ Author Affiliations
- Author Affiliations
A Engineering Risk Analysis Group, Technische Universitaet München, Theresienstrasse 90, 80333, Munich, Germany.
B Hellenic Agricultural Organisation ‘Demeter’, Institute of Mediterranean Forest Ecosystems, Terma Alkmanos, 11528, Athens, Greece.
C Corresponding author. Email: patty.papakosta@gmail.com
International Journal of Wildland Fire 26(1) 10-23 https://doi.org/10.1071/WF15113
Submitted: 10 June 2015 Accepted: 13 October 2016 Published: 11 January 2017
Journal Compilation © IAWF 2017 Open Access CC BY-NC-ND
Abstract
Loss prediction models are an important part of wildfire risk assessment, but have received only limited attention in the scientific literature. Such models can support decision-making on preventive measures targeting fuels or potential ignition sources, on fire suppression, on mitigation of consequences and on effective allocation of funds. This paper presents a probabilistic model for predicting wildfire housing loss at the mesoscale (1 km2) using Bayesian network (BN) analysis. The BN enables the construction of an integrated model based on causal relationships among the influencing parameters jointly with the associated uncertainties. Input data and models are gathered from literature and expert knowledge to overcome the lack of housing loss data in the study area. Numerical investigations are carried out with spatiotemporal datasets for the Mediterranean island of Cyprus. The BN is coupled with a geographic information system (GIS) and the resulting estimated house damages for a given fire hazard are shown in maps. The BN model can be attached to a wildfire hazard model to determine wildfire risk in a spatially explicit manner. The developed model is specific to areas with house characteristics similar to those found in Cyprus, but the general methodology is transferable to any other area, as well as other damages.
Additional keywords: loss prediction, Mediterranean, vulnerability.
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