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Bayesian decision network modeling for environmental risk management: a wildfire case study

Author(s): Trent D. Penman, Brett Cirulis, Bruce G. Marcot
Year Published: 2020

Environmental decision-making requires an understanding of complex interacting systems across scales of space and time. A range of statistical methods, evaluation frameworks and modeling approaches have been applied for conducting structured environmental decision-making under uncertainty. Bayesian Decision Networks (BDNs) are a useful construct for addressing uncertainties in environmental decision-making. In this paper, we apply a BDN to decisions regarding fire management to evaluate the general efficacy and utility of the approach in resource and environmental decision-making. The study was undertaken in south-eastern Australia to examine decisions about prescribed burning rates and locations based on treatment and impact costs. Least-cost solutions were identified but are unlikely to be socially acceptable or practical within existing resources; however, the statistical approach allowed for the identification of alternative, more practical solutions. BDNs provided a transparent and effective method for a multi-criteria decision analysis of environmental management problems.

Citation: Penman, Trent D.; Cirulis, Brett; Marcot, Bruce G. 2020. Bayesian decision network modeling for environmental risk management: a wildfire case study. Journal of Environmental Management 270:110735. https://doi.org/10.1016/j.jenvman.2020.110735
Topic(s): Fuels, Fuel Treatments & Effects, Prescribed Fire-use treatments, Fuels Inventory & Monitoring, Risk
Ecosystem(s): None
Document Type: Book or Chapter or Journal Article
NRFSN number: 22173
FRAMES RCS number: 61339
Record updated: Nov 5, 2020