Digital twins as a security risk?

Digital 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.

Artificial intelligence for risk analysis-A risk characterization perspective on advances, opportunities, and limitations.

Artificial intelligence (AI) has seen numerous applications for risk analysis and provides ample opportunities for developing new and improved methods and models for this purpose. In the present article, we conceptualize the use of AI for risk analysis by framing it as an input-algorithm-output process and linking such a setup to three tasks in establishing a risk description: consequence characterization, uncertainty characterization, and knowledge management. We then give an overview of currently used concepts and methods for AI-based risk analysis and outline potential future uses by extrapolating beyond currently produced types of output.

Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences.

Demands to manage the risks of artificial intelligence (AI) are growing. These demands and the government standards arising from them both call for trustworthy AI. In response, we adopt a convergent approach to review, evaluate, and synthesize research on the trust and trustworthiness of AI in the environmental sciences and propose a research agenda.

Impacts of climate change on cassava yield and lifecycle energy and greenhouse gas performance of cassava ethanol systems: An example from Guangxi Province, China.

Significant shock of climate change on crop yield will challenge the performance of bio-crop on substituting fossil energy to mitigate climate change. Taking cassava-to-ethanol system in Guangxi Province of South China as an example, we coupled a random forest (RF) model with 10 Global climate models (GCMs) outputs to predict the future cassava yields. Subsequently, the net energy value (NEV) and greenhouse gas (GHG) emissions of the cassava-to-ethanol system across varied topographies are assessed using a life cycle analysis.

Power outage prediction using data streams: An adaptive ensemble learning approach with a feature- and performance-based weighting mechanism.

A wide variety of weather conditions, from windstorms to prolonged heat events, can substantially impact power systems, posing many risks and inconveniences due to power outages. Accurately estimating the probability distribution of the number of customers without power using data about the power utility system and environmental and weather conditions can help utilities restore power more quickly and efficiently. However, the critical shortcoming of current models lies in the difficulties of handling (i) data streams and (ii) model uncertainty due to combining data from various weather events.

A data-driven method for identifying the locations of hurricane evacuations from mobile phone location data.

How evacuations are managed can substantially impact the risks faced by affected communities. Having a better understanding of the mobility patterns of evacuees can improve the planning and management of these evacuations. Although mobility patterns during evacuations have traditionally been studied through surveys, mobile phone location data can be used to capture these movements for a greater number of evacuees over a larger geographic area.

Using mobile phone data to evaluate access to essential services following natural hazards.

Natural 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.

Data-driven predictive modeling in risk assessment: Challenges and directions for proper uncertainty representation.

Data-driven predictive modeling is increasingly being used in risk assessments. While such modeling may provide improved consequence predictions and probability estimates, it also comes with challenges. One is that the modeling and its output does not measure and represent uncertainty due to lack of knowledge, that is, "epistemic uncertainty.

Improved Decision-Making: A Sociotechnical Utility-Based Framework for Drinking Water Investment.

To achieve the goals of the Safe Drinking Water Act, state and local water authorities need to make decisions about where to direct limited funding for infrastructure improvements and currently do so in the absence of adequate evaluative metrics. We developed a framework grounded in utility theory that compares trade-offs explicitly and broadens the factors considered in prioritizing resource allocations. Relevant existing indices were reviewed to identify data applicable to drinking water decision-making.

COVID-19 aerosol transmission simulation-based risk analysis for in-person learning.

As educational institutions begin a school year following a year and a half of disruption from the COVID-19 pandemic, risk analysis can help to support decision-making for resuming in-person instructional operation by providing estimates of the relative risk reduction due to different interventions. In particular, a simulation-based risk analysis approach enables scenario evaluation and comparison to guide decision making and action prioritization under uncertainty. We develop a simulation model to characterize the risks and uncertainties associated with infections resulting from aerosol exposure in in-person classes.

Individual learning as a driver of changes in community vulnerability under repeated hurricanes and changing climate.

The risks from singular natural hazards such as a hurricane have been extensively investigated in the literature. However, little is understood about how individual and collective responses to repeated hazards change communities and impact their preparation for future events. Individual mitigation actions may drive how a community's resilience evolves under repeated hazards.

A machine learning approach for the prediction of overall deceased donor organ yield.

Background: Optimizing organ yield (number of organs transplanted per donor) is a potentially modifiable way to increase the number of organs available for transplant. Models to predict the expected deceased donor organ yield have been developed based on ordinary least squares regression and logistic regression. However, alternative modeling methodologies incorporating machine learning may have superior performance compared with conventional approaches.

Interdisciplinary Theory, Methods, and Approaches for Hazards and Disaster Research: An Introduction to the Special Issue.

What 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.

The Role of Time in Risk and Risk Analysis: Implications for Resilience, Sustainability, and Management.

There is a persistent misconception that risk analysis is only suited for considering the immediate consequences of an event. Such a limitation would make risk analysis unsuitable for many challenges, including resilience, sustainability, and adaptation. Fortunately, there is no such limitation.

Tropical Cyclone Exposures and Risks of Emergency Medicare Hospital Admission for Cardiorespiratory Diseases in 175 Urban United States Counties, 1999-2010.

Background: Although injuries experienced during hurricanes and other tropical cyclones have been relatively well-characterized through traditional surveillance, less is known about tropical cyclones' impacts on noninjury morbidity, which can be triggered through pathways that include psychosocial stress or interruption in medical treatment.

Methods: We investigated daily emergency Medicare hospitalizations (1999-2010) in 180 US counties, drawing on an existing cohort of high-population counties. We classified counties as exposed to tropical cyclones when storm-associated peak sustained winds were ≥21 m/s at the county center; secondary analyses considered other wind thresholds and hazards.

Protective impacts of household-based tuberculosis contact tracing are robust across endemic incidence levels and community contact patterns.

There is an emerging consensus that achieving global tuberculosis control targets will require more proactive case finding approaches than are currently used in high-incidence settings. Household contact tracing (HHCT), for which households of newly diagnosed cases are actively screened for additional infected individuals is a potentially efficient approach to finding new cases of tuberculosis, however randomized trials assessing the population-level effects of such interventions in settings with sustained community transmission have shown mixed results. One potential explanation for this is that household transmission is responsible for a variable proportion of population-level tuberculosis burden between settings.

On the Use of Standards and Guidelines as a Tool to Fulfil Regulatory Requirements.

Over the years, industrial safety regulation has shifted from a "hard" command and control regime to a "soft" regime. A "hard" regime includes the use of strict prescriptive requirements which explain how industry should solve particular issues. A "soft" regime, uses more functional requirements, pointing out what goals are to be achieved.

Risk Factors for 100-Year Flood Events in the Mid-Atlantic Region of the United States.

Anecdotal 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.

Assessing United States County-Level Exposure for Research on Tropical Cyclones and Human Health.

Background: Tropical cyclone epidemiology can be advanced through exposure assessment methods that are comprehensive and consistent across space and time, as these facilitate multiyear, multistorm studies. Further, an understanding of patterns in and between exposure metrics that are based on specific hazards of the storm can help in designing tropical cyclone epidemiological research.

Objectives: ) Provide an open-source data set for tropical cyclone exposure assessment for epidemiological research; and ) investigate patterns and agreement between county-level assessments of tropical cyclone exposure based on different storm hazards.

Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs.

Artificial 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.

Reframing Resilience: Equitable Access to Essential Services.

We urgently need to put the concept of resilience into practice if we are to prepare our communities for climate change and exacerbated natural hazards. Yet, despite the extensive discussion surrounding community resilience, operationalizing the concept remains challenging. The dominant approaches for assessing resilience focus on either evaluating community characteristics or infrastructure functionality.

Simulating Behavioral Influences on Community Flood Risk under Future Climate Scenarios.

Flood risk is a function of both climate and human behavior, including individual and societal actions. For this reason, there is a need to incorporate both human and climatic components in models of flood risk. This study simulates behavioral influences on the evolution of community flood risk under different future climate scenarios using an agent-based model (ABM).

Review and Evaluation of the J100-10 Risk and Resilience Management Standard for Water and Wastewater Systems.

Risk analysis standards are often employed to protect critical infrastructures, which are vital to a nation's security, economy, and safety of its citizens. We present an analysis framework for evaluating such standards and apply it to the J100-10 risk analysis standard for water and wastewater systems. In doing so, we identify gaps between practices recommended in the standard and the state of the art.