TY - JOUR
T1 - A spatially explicit decision support framework for parcel- and community-level resilience assessment using Bayesian networks
AU - Sanderson, Dylan
AU - Cox, Daniel
AU - Naraharisetty, Gowtham
N1 - Funding Information:
for this study was provided as part of the cooperative agreement 70NANB15H044 between the National Institute of Standards and Technology (NIST) and Colorado State University through a subaward to Oregon State University. The content expressed in this paper are the views of the authors and do not necessarily represent the opinions or views of NIST or the U.S Department of Commerce
Publisher Copyright:
© 2021 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2022
Y1 - 2022
N2 - A spatially explicit decision support framework for quantifying parcel- and community-level resilience against natural hazards is developed using Bayesian networks. Rather than asking ‘how resilient are infrastructure systems in a community’, this work reframes that question to ask, ‘how resilient are parcels in a community given that they rely on these same infrastructure systems’. This framework begins with a probabilistic infrastructure damage analysis which results in operability curves that define the initial loss in infrastructure performance at each parcel and subsequent time to recover. The operability curves populate Bayesian networks, which are then employed as a decision support tool to generate maps of resilience. By applying this framework to Seaside, Oregon and considering seismic-tsunami events, we show (1) that overall resilience is low for the 1,000-yr event under status quo conditions, (2) how the resilience improves with mitigation options, and (3) that the testbed is least resilient to mid-magnitude events.
AB - A spatially explicit decision support framework for quantifying parcel- and community-level resilience against natural hazards is developed using Bayesian networks. Rather than asking ‘how resilient are infrastructure systems in a community’, this work reframes that question to ask, ‘how resilient are parcels in a community given that they rely on these same infrastructure systems’. This framework begins with a probabilistic infrastructure damage analysis which results in operability curves that define the initial loss in infrastructure performance at each parcel and subsequent time to recover. The operability curves populate Bayesian networks, which are then employed as a decision support tool to generate maps of resilience. By applying this framework to Seaside, Oregon and considering seismic-tsunami events, we show (1) that overall resilience is low for the 1,000-yr event under status quo conditions, (2) how the resilience improves with mitigation options, and (3) that the testbed is least resilient to mid-magnitude events.
KW - Community resilience
KW - multi-hazard
KW - parcel resilience
KW - seismic-tsunami
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U2 - 10.1080/23789689.2021.1966164
DO - 10.1080/23789689.2021.1966164
M3 - Article
AN - SCOPUS:85114422581
SN - 2378-9689
VL - 7
SP - 531
EP - 551
JO - Sustainable and Resilient Infrastructure
JF - Sustainable and Resilient Infrastructure
IS - 5
ER -