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AbstractArtificial intelligence (AI) methods have seen increasingly widespread use in everything from consumer products and driverless cars to fraud detection and weather forecasting. The use of AI has transformed many of these application domains. There are ongoing efforts at leveraging AI for disaster risk analysis. This article takes a critical look at the use of AI for disaster risk analysis. What is the potential? How is the use of AI in this field different from its use in nondisaster fields? What challenges need to be overcome for this potential to be realized? And, what are the potential pitfalls of an AI‐based approach for disaster risk analysis that we as a society must be cautious of?

Intelligent adversary modeling has become increasingly important for risk analysis, and a number of different approaches have been proposed for incorporating intelligent adversaries in risk analysis models. However, these approaches are based on a range of often‐implicit assumptions about the desirable properties of intelligent adversary models. This "Perspective" paper aims to further risk analysis for situations involving intelligent adversaries by fostering a discussion of the desirable properties for these models. A set of four basic necessary conditions for intelligent adversary models is proposed and discussed. These are: (1) behavioral accuracy to the degree possible, (2) computational tractability to support decision making, (3) explicit consideration of uncertainty, and (4) ability to gain confidence in the model. It is hoped that these suggested necessary conditions foster discussion about the goals and assumptions underlying intelligent adversary modeling in risk analysis.

AbstractDigital twins have become a popular and widely used tool for assessing risk and resilience, particularly as they have increased in the fidelity and accuracy of their representation of real‐world systems. Although digital twins provide the ability to experiment on and assess risks to and from a system without damaging the real‐world system, they pose potentially significant security risks. For example, if a digital twin of a power system has sufficient accuracy to allow loss of electrical power service due to a natural hazard to be estimated at the address level with a high degree of accuracy, what prevents someone wishing to lead to disruption at this same building from using the model to solve the inverse problem to determine which parts of the power system should be attacked to maximize the likelihood of loss of service to the target facility? This perspective article discusses the benefits and risks of digital twins and argues that more attention needs to be paid to the risks posed by digital twins.

AbstractNatural hazards bring about changes in the access to essential services such as grocery stores, healthcare, schools, and day care because of facility closures, transportation system disruption, evacuation orders, power outages, and other barriers to access. Understanding changes in access to essential services following a disruption is critical to ensure equitable recovery and more resilient communities. However, past approaches to understanding facility closures and inaccessibility such as surveys and interviews are labor‐intensive and of limited geographic scope. In this article, we develop an approach to understanding facility‐level inaccessibility across a broad geographic area based on location‐based services data collected from cell phones. This approach supplements current approaches and helps both researchers and emergency response planners better understand which communities lose access to essential services and for how long. We demonstrate our approach by analyzing loss of access to supermarkets, schools, healthcare facilities, and home improvement stores in Southwest Florida leading up to and following the landfall of Hurricane Irma in 2017.

AbstractWhat is interdisciplinary research? Why is it vital to the advancement of the field of hazards and disaster research? What theory, methods, and approaches are fundamental to interdisciplinary research projects and their applications? This article addresses these and other pressing questions by taking stock of recent advancements in interdisciplinary studies of hazards and disasters. It also introduces the special issue of Risk Analysis, which includes this introductory article and 25 original perspectives papers meant to highlight new trends and applications in the field. The papers were written following two National Science Foundation‐supported workshops that were organized in response to the growing interest in interdisciplinary hazards and disaster research, the increasing number of interdisciplinary funding opportunities and collaborations in the field, and the need for more rigorous guidance for interdisciplinary researchers and research teams. This introductory article and the special collection are organized around the cross‐cutting themes of theory, methods, approaches, interdisciplinary research projects, and applications to advance interdisciplinarity in hazards and disaster research.

AbstractAnecdotal information indicates that streams in the Mid‐Atlantic region of the United States experience more extreme flood events than might be expected. This leads to the question of whether this is an unfounded perception or if these extreme events are actually occurring more than should be expected. If the latter is true, is this due solely to randomness, or alternately to characteristics that make certain watersheds more prone to repeated events that may be defined as 100‐year or greater floods? These questions are investigated through analysis of flood events based on standard flood frequency analysis. 100‐year streamflow rates for stream gages were estimated using Bulletin 17B flood frequency analysis methods, and the probability of the annual peak flow record for each gage was calculated. These probabilities were compared to a set of synthetic probabilities to evaluate their distribution. This comparison indicates that for the Mid‐Atlantic region as a whole, the Bulletin 17B method does not systematically over or underestimate flood frequency. A Random Forest model of probability of actual flood record (PAFR) versus watershed and stream gage characteristics was developed and used to understand if certain characteristics are associated with PAFR. This analysis indicated that unexpected numbers of large flood events in a stream gage period of record can be attributed primarily to randomness, but there is some correlation with watershed and gage characteristics including weighted skew, drainage area, and mean annual peak discharge. The results indicate that watersheds with high values of these characteristics may warrant advanced flood frequency methods.

In this article, we provide some reflections on how to define and understand the concept of terrorism risk in a professional risk assessment context. As a basis for this discussion we introduce a set of criteria that we believe should apply to any conceptualization of terrorism risk. These criteria are based on both criteria used in other areas of risk analysis and our experience with terrorism risk analysis. That is, these criteria offer our perspective. We show that several of the suggested perspectives and definitions have weaknesses in relation to these criteria. A main problem identified is the idea that terrorism risk can be conceptualized as a function of probability and consequence, not as a function of the interactions between adaptive individuals and organizations. We argue that perspectives based solely on probability and consequence should be used cautiously or not at all because they fail to reflect the essential features of the concept of terrorism risk, the threats and attacks, their consequences, and the uncertainties, all in the context of adaptation by the adversaries. These three elements should in our view constitute the main pillars of the terrorism risk concept. From this concept we can develop methods for assessing the risk by identifying a set of threats, attacks, and consequence measures associated with the possible outcome scenarios together with a description of the uncertainties and interactions between the adversaries.

Wind power is becoming an increasingly important part of the global energy portfolio, and there is growing interest in developing offshore wind farms in the United States to better utilize this resource. Wind farms have certain environmental benefits, notably near‐zero emissions of greenhouse gases, particulates, and other contaminants of concern. However, there are significant challenges ahead in achieving large‐scale integration of wind power in the United States, particularly offshore wind. Environmental impacts from wind farms are a concern, and these are subject to a number of on‐going studies focused on risks to the environment. However, once a wind farm is built, the farm itself will face a number of risks from a variety of hazards, and managing these risks is critical to the ultimate achievement of long‐term reductions in pollutant emissions from clean energy sources such as wind. No integrated framework currently exists for assessing risks to offshore wind farms in the United States, which poses a challenge for wind farm risk management. In this "Perspective", we provide an overview of the risks faced by an offshore wind farm, argue that an integrated framework is needed, and give a preliminary starting point for such a framework to illustrate what it might look like. This is not a final framework; substantial work remains. Our intention here is to highlight the research need in this area in the hope of spurring additional research about the risks to wind farms to complement the substantial amount of on‐going research on the risks from wind farms.

The federal government's allocation of resources for defense against potential attacks generally involves depending on a multitiered organization consisting of federal, state, and local agencies for information on the risks faced in their jurisdictions and the costs and benefits of defensive actions they may take. These agencies then receive resources, ideally based on the collectively reported risks and resource needs. With private information about local risks and defensive actions and limited resources at the federal level, there are ample opportunities for agencies to take advantage of such a system for their benefit. This yields a suboptimal allocation of limited defensive resources. In this paper we describe this allocation problem formally as a game between a single principal (e.g., Congress) and agents representing more local agencies. This differs substantially from the treatment of the attacker–defender problem in the literature, where the defender is treated as a single, unified decision maker. We show that ignoring the within-team defender interactions in modeling the attacker–defender game leads to a suboptimal resource allocation. Existing results from agency theory are applied to this problem together with new results and insights into the interactions between an attacker and a multilevel defender.