Sensitivity Analysis of Tsunami Evacuation Risk with Respect to Epistemic Uncertainty
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8, Issue 3
Abstract
To assess tsunami evacuation risk accurately in order to guide effective evacuation planning, various uncertainties (including the aleatory and epistemic uncertainties) associated with the evacuation process need to be quantified properly. Reducing the epistemic uncertainties associated with the evacuation process (e.g., through data collection) can facilitate more-accurate estimation of tsunami evacuation risk. To guide such reduction or prioritize data collection, this study performed sensitivity analysis of tsunami evacuation risk (i.e., risk sensitivity analysis) with respect to epistemic uncertainty. An agent-based tsunami evacuation model was used to simulate the evacuation within a simulation-based risk assessment framework, which incorporated various uncertainties associated with the evacuation process. The aleatory uncertainty in the input random variable was quantified by probability distribution models, and the epistemic uncertainties were quantified by distribution parameters that also were modeled by probability distributions. Sensitivity analysis of tsunami evacuation risk with respect to the epistemic uncertainty was performed to evaluate the impact of various epistemic uncertainties on the variability of the evacuation risk and identify those that have relatively large impacts. An augmented sample-based approach was used to calculate efficiently the variance-based sensitivity indexes (i.e., Sobol’ indexes) for all distribution parameters. The sensitivity information can be used to prioritize the data collection for effective epistemic uncertainty reduction, and for a more accurate risk assessment to support more-effective evacuation planning. As an illustrative example, sensitivity analysis of tsunami evacuation risk of Seaside, Oregon with respect to epistemic uncertainty was performed under different risk measures.
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Data Availability Statement
All data, models, and code that support the findings of this study are available from the corresponding author upon reasonable request.
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8 • Issue 3 • September 2022
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© 2022 American Society of Civil Engineers.
History
Received: Nov 10, 2021
Accepted: Apr 12, 2022
Published online: Jul 5, 2022
Published in print: Sep 1, 2022
Discussion open until: Dec 5, 2022
ASCE Technical Topics:
- Analysis (by type)
- Continuum mechanics
- Data analysis
- Data collection
- Disaster preparedness
- Disaster risk management
- Disasters and hazards
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Evacuation
- Mathematics
- Methodology (by type)
- Motion (dynamics)
- Natural disasters
- Probability
- Research methods (by type)
- Risk management
- Sensitivity analysis
- Solid mechanics
- Tsunamis
- Uncertainty principles
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