Integrated Decision Support for Disaster Risk Management: Aiding Preparedness and Response Decisions in Wildfire Management

A central challenge in disaster risk management (DRM) is that there are key dependencies and uncertainty between the decisions made at the mitigation, preparedness, response, and recovery stages. Evaluating the impact of strategic decisions on the decisions and outcomes of subsequent stages is paramount to determine informed risk management policies (e.g., estimating the risk reduction that may be achieved by a mitigation strategy relative to its cost). Performing such analyses is difficult, not only because of the uncertainty inherent to disastrous events but because it implies integrating the logic and data of different processes that occur at each stage. Comprehensive decision support systems for disaster management and thus require information systems that allow timely and reliable integration of data sources from different domains, including information on hazards and vulnerabilities for risk analysis and organizational and logistical information for decision analysis. We propose an analytics-centered framework that integrates predictive and prescriptive models responding to unique characteristics of DRM. The framework relies on probabilistic risk assessment and uses optimization-based simulation of the response phase as a means to inform decisions at the preparedness stage. This paper presents a case study regarding the analysis of preparedness and response decisions for wildfire control in Uruguay. Numerical results illustrate the insights that can be derived from the integration of data and models at multiple stages. Specifically, in the Uruguay case, slight reductions in the preparedness budget can lead to disproportionate losses during the response stage, whereas slight increases have little effect unless explicitly directed to control high-consequence scenarios. Motivated by a real-world problem, this case study emphasizes the challenges for integrated information systems that enable the potential of analytical decision support frameworks for DRM.