Probabilistic prediction of wildfire economic losses to housing in Cyprus using Bayesian network analysis

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.