A Decision Framework for Pre-disaster Risk Mitigation Planning of Interdependent Infrastructure Systems

Implementing strategic pre-disaster risk mitigation plan to improve the resilience of the interdependent civil infrastructure systems is essential for enhancing the social security and economic prosperity of a community. Majority of the existing infrastructure risk mitigation studies or projects focus on a single infrastructure system. However, improving the resilience of a single infrastructure system under disasters may not be the most efficient and effective way to mitigate the loss and enhance the overall community disaster resilience. This is because the proper functioning of the facilities in an infrastructure system usually depends on the normal operation of facilities in several other infrastructure systems for product input and information sharing. This article presents a risk-informed decision framework which could support the pre-disaster risk mitigation planning of several interdependent infrastructure systems. The characteristics of the Interdependent Infrastructure Risk Mitigation (IIRM) decision problem, such as objective, decision makers, constraints, etc., are clearly identified. A four-stage decision framework to solve the IIRM problem is also presented. One novel contribution of this decision framework is that it considers the pre-decision processing step, which prioritizes the infrastructure facilities in different systems for risk mitigation investment and intervention. The application of the proposed IIRM decision framework is illustrated using a case study on pre-disaster risk mitigation planning for the interdependent critical infrastructure systems in Jamaica. The outcome of the IIRM problem is useful for the decision makers to allocate limited risk mitigation budget or resources to the most critical infrastructure facilities in different systems to achieve greater community disaster resilience.This project is funded by the National Institute of Standard and Technology (NIST) through Center for Risk-Based Community Resilience Planning. Frederico Ferreira Fonse Pedroso, Diana Cubas, Sideney Schreiner and Xijie Lv from the World Bank are gratefully acknowledged for providing some of the input data needed for the case study