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

Abstract

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 km 2 ) 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.

Probabilistic Decision Tools for Determining Impacts of Agricultural Development Policy on Household Nutrition

“…Conditional Probability Tables (CPTs) are the core elements of BN models (Fenton & Neil, ). They are used to define the probabilities for each state of each child node conditional on its parents (Papakosta et al, ). Ranked‐scale nodes can be used to represent most relationships, with states (e.g., very low, low, medium, high, very high; Figure ) representing qualitative variables as abstractions of the underlying continuous quantities.…”

“…This gives BN models the potential to reveal key features of complex systems (Constantinou et al, ; Lewis & McCormick, ) and may make them preferable to standard approaches for inferring statistical dependencies from complex observational data (Korb & Nicholson, ; Lewis & McCormick, ). Another major advantage of BN models is that they facilitate integration of information from various sources into a single model (Papakosta et al, ). For example, they can be used to build predictive models of impact pathways that incorporate both hard data and expert judgment (Yet et al, ).…”

“…This is then complemented with innovative group work techniques for eliciting expert knowledge to construct a logical framework to describe system interactions and outcomes (i.e., an impact pathway). Expert knowledge is thereby used to generate BN model structures (Bolger & Rowe, ; Kuhnert et al, ; Papakosta et al, ) and integrated into model calculations (Yet et al, ).…”

“…Expert‐based model construction can be facilitated through holistic expert knowledge elicitation (EKE) techniques that include the holders of scientific and political as well as traditional knowledge (Bolger & Wright, ). Papakosta et al () created a BN, through the integration of both EKE and literature sources, to predict expected economic losses (housing losses) from wildfires in Cyprus. Iqbal and MacLean () presented BN models for predicting defoliation risks in forests, which were produced during repeated expert meetings and subsequently subjected to peer review by other experts.…”

Analyzing Wildfire Suppression Difficulty in Relation to Protection Demand

“…These efforts reflect broader trends of increasing sophistication of risk assessment and risk management within the global fire science and decision support communities [11][12][13][14][15][16]. One notable advancement relates to analysis of risk transmission (i.e., the analysis of ignition patterns, potential fire flow pathways, and simulated fire perimeters) in order to determine areas on the landscape that have a higher propensity to be a source of damaging fires [17][18][19][20][21].…”