@article{ref1,
title="Bayesian decision network modeling for environmental risk management: a wildfire case study",
journal="Journal of environmental management",
year="2020",
author="Penman, Trent D. and Cirulis, Brett and Marcot, Bruce G.",
volume="270",
number="",
pages="e110735-e110735",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="0301-4797",
doi="10.1016/j.jenvman.2020.110735",
url="http://dx.doi.org/10.1016/j.jenvman.2020.110735"
}