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Sensitivity Analysis of Tsunami Evacuation Risk with Respect to Epistemic Uncertainty
https://orcid.org/0000-0001-9419-8481
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.
Sensitivity Analysis of Tsunami Evacuation Risk with Respect to Epistemic Uncertainty
https://orcid.org/0000-0001-9419-8481
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.
Sensitivity Analysis of Tsunami Evacuation Risk with Respect to Epistemic Uncertainty
https://orcid.org/0000-0001-9419-8481
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.
Sensitivity Analysis of Tsunami Evacuation Risk with Respect to Epistemic Uncertainty
ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.
Wang, Zhenqiang (author) / Jia, Gaofeng (author)
2022-09-01
Article (Journal)
Electronic Resource
English