Infrastructure Recovery Curve Estimation Using Gaussian Process Regression on Expert Elicited Data

TL;DR

This paper introduces a Gaussian process regression framework to estimate infrastructure recovery curves using expert-elicited data, effectively balancing data collection costs and prediction accuracy for disaster management.

Contribution

It presents a novel methodological framework that incorporates physical constraints and uncertainty modeling in recovery time estimation from expert opinions.

Findings

Effective recovery curve estimation demonstrated on simulated data.

Framework captures expert uncertainty and physical constraints.

Applicable to real-world disaster recovery planning.

Abstract

Infrastructure recovery time estimation is critical to disaster management and planning. Inspired by recent resilience planning initiatives, we consider a situation where experts are asked to estimate the time for different infrastructure systems to recover to certain functionality levels after a scenario hazard event. We propose a methodological framework to use expert-elicited data to estimate the expected recovery time curve of a particular infrastructure system. This framework uses the Gaussian process regression (GPR) to capture the experts' estimation-uncertainty and satisfy known physical constraints of recovery processes. The framework is designed to find a balance between the data collection cost of expert elicitation and the prediction accuracy of GPR. We evaluate the framework on realistically simulated expert-elicited data concerning the two case study events, the 1995 Great Hanshin-Awaji Earthquake and the 2011 Great East Japan Earthquake.