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. 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.

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.

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. Accordingly, this article proposes an adaptive ensemble learning algorithm for data streams, which deploys a feature- and performance-based weighting mechanism to adaptively combine outputs from multiple competitive base learners. As a proof of concept, we use a large, real data set of daily customer interruptions to develop the first adaptive all-weather outage prediction model using data streams. We benchmark several approaches to demonstrate the advantage of our approach in offering more accurate probabilistic predictions. The results show that the proposed algorithm reduces the probabilistic predictions' error of the base learners between 4% and 22% with an average of 8%, which also result in substantially more accurate point predictions. The improvement made by our algorithm is enhanced as we exchange base learners with simpler models.

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. Several approaches have been used to identify hurricane evacuation patterns from location data; however, each approach relies on researcher judgment to first determine the areas from which evacuations occurred and then identify evacuations by determining when an individual spends a specified number of nights away from home. This approach runs the risk of detecting non-evacuation behaviors (e.g., work trips, vacations, etc.) and incorrectly labeling them as evacuations where none occurred. In this article, we developed a data-driven method to determine which areas experienced evacuations. With this approach, we inferred home locations of mobile phone users, calculated their departure times, and determined if an evacuation may have occurred by comparing the number of departures around the time of the hurricane against historical trends. As a case study, we applied this method to location data from Hurricanes Matthew and Irma to identify areas that experienced evacuations and illustrate how this method can be used to detect changes in departure behavior leading up to and following a hurricane. We validated and examined the inferred homes for representativeness and validated observed evacuation trends against past studies.

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. We end with a discussion of the limits of automation, both near-term limitations and a more fundamental question related to allowing AI to automatically prescribe risk management decisions. We conclude that there are opportunities for using AI for risk analysis to a greater extent than is commonly the case today; however, critical concerns about proper uncertainty representation and the need for risk-informed rather than risk-based decision-making also lead us to conclude that risk analysis and decision-making processes cannot be fully automated.

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. In this paper, we investigate the effect that learning by homeowners can have on household mitigation decisions and on how this influences a region's vulnerability to natural hazards over time, using hurricanes along the east coast of the United States as our case study. To do this, we build an agent-based model (ABM) to simulate homeowners' adaptation to repeated hurricanes and how this affects the vulnerability of the regional housing stock. Through a case study, we explore how different initial beliefs about the hurricane hazard and how the memory of recent hurricanes could change a community's vulnerability both under current and potential future hurricane scenarios under climate change. In some future hurricane environments, different initial beliefs can result in large differences in the region's long-term vulnerability to hurricanes. We find that when some homeowners mitigate soon after a hurricane-when their memory of the event is the strongest-it can help to substantially decrease the vulnerability of a community.

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." In this article, we demonstrate this point by conceptually linking the main elements and output of data-driven predictive models with the main elements of a general risk description, thereby placing data-driven predictive modeling on a risk science foundation. This allows for an evaluation of such modeling with reference to risk science recommendations for what constitutes a complete risk description. The evaluation leads us to conclude that, as a minimum, to cover all elements of a complete risk description a risk assessment using data-driven predictive modeling needs to be supported by assessments of the uncertainty and risk related to the assumptions underlying the modeling. In response to this need, we discuss an approach for assessing assumptions in data-driven predictive modeling.

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. Evidential and conceptual histories of research on trust and trustworthiness reveal persisting ambiguities and measurement shortcomings related to inconsistent attention to the contextual and social dependencies and dynamics of trust. Potentially underappreciated in the development of trustworthy AI for environmental sciences is the importance of engaging AI users and other stakeholders, which human-AI teaming perspectives on AI development similarly underscore. Co-development strategies may also help reconcile efforts to develop performance-based trustworthiness standards with dynamic and contextual notions of trust. We illustrate the importance of these themes with applied examples and show how insights from research on trust and the communication of risk and uncertainty can help advance the understanding of trust and trustworthiness of AI in the environmental sciences.

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. We demonstrate that the abrupt increases in temperatures are the primary contributors to declining yields. Notably, cassava yields in hilly regions decline more than those in plains and display greater variability among concentration pathway scenarios over time. Future NEV and GHG performance of cassava-to-ethanol will undergo significant decreases over time, especially within the high concentration pathway scenario (NEV decrease 28%, GHG increase 3.4% from 2006 to 2100). The performance reductions in hilly area are exacerbated by more harvest loss and labor and material inputs during the "field-to-wheel", negating its energy advantage over fossil fuels. Therefore, adopting a lower concentration pathway and favoring plantation in plains could maintain cassava-to-ethanol as a viable climate mitigation strategy. Our research also advances the methodological approach to climate change adaptation within the domain of life cycle assessment.

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. For example, transmission is more likely to occur in households in settings with a lower tuberculosis burden and where individuals mix preferentially in local areas, compared with settings with higher disease burden and more dispersed mixing. To better understand the relationship between endemic incidence levels, social mixing, and the impact of HHCT, we developed a spatially explicit model of coupled household and community transmission. We found that the impact of HHCT was robust across settings of varied incidence and community contact patterns. In contrast, we found that the effects of community contact tracing interventions were sensitive to community contact patterns. Our results suggest that the protective benefits of HHCT are robust and the benefits of this intervention are likely to be maintained across epidemiological settings.

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. We matched storm-exposed days to unexposed days by county and seasonality. We estimated change in tropical cyclone-associated hospitalizations over a storm period from 2 days before to 7 days after the storm's closest approach, compared to unexposed days, using generalized linear mixed-effect models. RESULTS: For 1999-2010, 175 study counties had at least one tropical cyclone exposure. Cardiovascular hospitalizations decreased on the storm day, then increased following the storm, while respiratory hospitalizations were elevated throughout the storm period. Over the 10-day storm period, cardiovascular hospitalizations increased 3% (95% confidence interval = 2%, 5%) and respiratory hospitalizations increased 16% (95% confidence interval = 13%, 20%) compared to matched unexposed periods. Relative risks varied across tropical cyclone exposures, with strongest association for the most restrictive wind-based exposure metric. CONCLUSIONS: In this study, tropical cyclone exposures were associated with a short-term increase in cardiorespiratory hospitalization risk among the elderly, based on a multi-year/multi-site investigation of US Medicare beneficiaries ≥65 years.

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. 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.

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. 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.

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. In a "soft" regime, prescriptive standards might still exist, but they are considered suggested solutions, with alternative solutions also being considered if they achieve the overall regulatory goals. The purpose of such a shift is to create regulations that are more flexible, meaning that they are more open for the use of novel technology and for the use of risk assessments as a basis for decision making. However, it is not clear that the shift from a hard to a soft regime has made it easier to use risk assessments for such a purpose in practice. In the present article, we discuss the limitations caused by strict adherence to prescriptive requirements presented in standards or regulations and present our perspective on why and how these can limit risk management in practice. The article aims to discuss the strengths and weaknesses, with regard to risk management, when regulations are strictly dependent on prescriptive or specification-based standards and guidelines. Several examples are used to illustrate some of the main challenges related to the use of specification-based technical standards and how the regulatory shift from "hard" to "soft" has not necessarily made it easier to implement technological solutions based on risk assessments.

Agent-Based Models as an Integrating Boundary Object for Interdisciplinary Research.

Many of the most complicated and pressing problems in hazards research require the integration of numerous disciplines. The lack of a common knowledge base, however, often prohibits clear communication and interaction among interdisciplinary researchers, sometimes leading to unsuccessful outcomes. Drawing on experience with several projects and collective expertise that spans multiple disciplines, the authors argue that a promising way to enhance participation and enable communication is to have a common model, or boundary object, that can integrate knowledge from different disciplines. The result is that researchers from different disciplines who use different research methods and approaches can work together toward a shared goal. This article offers four requirements for boundary objects that may enhance hazards research. Based on these requirements, agent-based models have the necessary characteristics to be a boundary object. The article concludes by examining both the value of and the challenges from using agent-based models as the boundary object in interdisciplinary projects.

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. However, this notion has stemmed from a lack of clarity regarding how time is considered in risk analysis and risk characterization. In this article, we discuss this issue and show that risk science provides concepts and frameworks that can appropriately address time. Ultimately, we propose an adjusted nomenclature for explicitly reflecting time in risk conceptualization and characterizations.

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. 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?

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). The objective is to understand better the ways, sometimes unexpected, that human behavior, stochastic floods, and community interventions interact to influence the evolution of flood risk. One historic climate scenario and three future climate scenarios are simulated using a case study location in Fargo, North Dakota. Individual agents can mitigate flood risk via household mitigation or by moving, based on decision rules that consider risk perception and coping perception. The community can mitigate or disseminate information to reduce flood risk. Results show that agent behavior and community action have a significant impact on the evolution of flood risk under different climate scenarios. In all scenarios, individual and community action generally result in a decline in damages over time. In a lower flood risk scenario, the decline is primarily due to agent mitigation, while in a high flood risk scenario, community mitigation and agent relocation are primary drivers of the decline. Adaptive behaviors offset some of the increase in flood risk associated with climate change, and under an extreme climate scenario, our model indicates that many agents relocate.

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. While both remain useful, they have several limitations to their ability to provide actionable insight. More importantly, the current conceptualizations do not consider essential services or how access is impaired by hazards. We argue that people need access to services such as food, education, health care, and cultural amenities, in addition to water, power, sanitation, and communications, to get back some semblance of normal life. Providing equitable access to these types of services and quickly restoring that access following a disruption are paramount to community resilience. We propose a new conceptualization of community resilience that is based on access to essential services. This reframing of resilience facilitates a new measure of resilience that is spatially explicit and operational. Using two illustrative examples from the impacts of Hurricanes Florence and Michael, we demonstrate how decisionmakers and planners can use this framework to visualize the effect of a hazard and quantify resilience-enhancing interventions. This "equitable access to essentials" approach to community resilience integrates with spatial planning, and will enable communities not only to "bounce back" from a disruption, but to "bound forward" and improve the resilience and quality of life for all residents.

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. While individual processes found within infrastructure risk analysis standards have been evaluated in the past, we present a foundational review and focus specifically on water systems. By highlighting both the conceptual shortcomings and practical limitations, we aim to prioritize the shortcomings needed to be addressed. Key findings from this study include (1) risk definitions fail to address notions of uncertainty, (2) the sole use of "worst reasonable case" assumptions can lead to mischaracterizations of risk, (3) analysis of risk and resilience at the threat-asset resolution ignores dependencies within the system, and (4) stakeholder values need to be assessed when balancing the tradeoffs between risk reduction and resilience enhancement.