Selecting Indicators for Assessing Community Sustainable Resilience

Communities are complex systems subject to a variety of hazards that can result in significant disruption to critical functions. Community resilience assessment is rapidly gaining popularity as a means to help communities better prepare for, respond to, and recover from disruption. Sustainable resilience, a recently developed concept, requires communities to assess system‐wide capability to maintain desired performance levels while simultaneously evaluating impacts to resilience due to changes in hazards and vulnerability over extended periods of time. To enable assessment of community sustainable resilience, we review current literature, consolidate available indicators and metrics, and develop a classification scheme and organizational structure to aid in identification, selection, and application of indicators within a dynamic assessment framework. A nonduplicative set of community sustainable resilience indicators and metrics is provided that can be tailored to a community's needs, thereby enhancing the ability to operationalize the assessment process.     

Stochastic Measures of Network Resilience: Applications to Waterway Commodity Flows

Given the ubiquitous nature of infrastructure networks in today's society, there is a global need to understand, quantify, and plan for the resilience of these networks to disruptions. This work defines network resilience along dimensions of reliability, vulnerability, survivability, and recoverability, and quantifies network resilience as a function of component and network performance. The treatment of vulnerability and recoverability as random variables leads to stochastic measures of resilience, including time to total system restoration, time to full system service resilience, and time to a specific α% resilience. Ultimately, a means to optimize network resilience strategies is discussed, primarily through an adaption of the Copeland Score for nonparametric stochastic ranking. The measures of resilience and optimization techniques are applied to inland waterway networks, an important mode in the larger multimodal transportation network upon which we rely for the flow of commodities. We provide a case study analyzing and planning for the resilience of commodity flows along the Mississippi River Navigation System to illustrate the usefulness of the proposed metrics.     

Probabilistic Multiple Hazard Resilience Model of an Interdependent Infrastructure System

Multiple hazard resilience is of significant practical value because most regions of the world are subject to multiple natural and technological hazards. An analysis and assessment approach for multiple hazard spatiotemporal resilience of interdependent infrastructure systems is developed using network theory and a numerical analysis. First, we define multiple hazard resilience and present a quantitative probabilistic metric based on the expansion of a single hazard deterministic resilience model. Second, we define a multiple hazard relationship analysis model with a focus on the impact of hazards on an infrastructure. Subsequently, a relationship matrix is constructed with temporal and spatial dimensions. Further, a general method for the evaluation of direct impacts on an individual infrastructure under multiple hazards is proposed. Third, we present an analysis of indirect multiple hazard impacts on interdependent infrastructures and a joint restoration model of an infrastructure system. Finally, a simplified two‐layer interdependent infrastructure network is used as a case study for illustrating the proposed methodology. The results show that temporal and spatial relationships of multiple hazards significantly influence system resilience. Moreover, the interdependence among infrastructures further magnifies the impact on resilience value. The main contribution of the article is a new multiple hazard resilience evaluation approach that is capable of integrating the impacts of multiple hazard interactions, interdependence of network components (layers), and restoration strategy.     

Inherent Costs and Interdependent Impacts of Infrastructure Network Resilience

Recent studies in system resilience have proposed metrics to understand the ability of systems to recover from a disruptive event, often offering a qualitative treatment of resilience. This work provides a quantitative treatment of resilience and focuses specifically on measuring resilience in infrastructure networks. Inherent cost metrics are introduced: loss of service cost and total network restoration cost. Further, “costs” of network resilience are often shared across multiple infrastructures and industries that rely upon those networks, particularly when such networks become inoperable in the face of disruptive events. As such, this work integrates the quantitative resilience approach with a model describing the regional, multi‐industry impacts of a disruptive event to measure the interdependent impacts of network resilience. The approaches discussed in this article are deployed in a case study of an inland waterway transportation network, the Mississippi River Navigation System.     

Toward General Principles for Resilience Engineering

Maintaining the performance of infrastructure-dependent systems in the face of surprises and unknowable risks is a grand challenge. Addressing this issue requires a better understanding of enabling conditions or principles that promote system resilience in a universal way. In this study, a set of such principles is interpreted as a group of interrelated conditions or organizational qualities that, taken together, engender system resilience. The field of resilience engineering identifies basic system or organizational qualities (e.g., abilities for learning) that are associated with enhanced general resilience and has packaged them into a set of principles that should be fostered. However, supporting conditions that give rise to such first-order system qualities remain elusive in the field. An integrative understanding of how such conditions co-occur and fit together to bring about resilience, therefore, has been less clear. This article contributes to addressing this gap by identifying a potentially more comprehensive set of principles for building general resilience in infrastructure-dependent systems. In approaching this aim, we organize scattered notions from across the literature. To reflect the partly self-organizing nature of infrastructure-dependent systems, we compare and synthesize two lines of research on resilience: resilience engineering and social-ecological system resilience. Although some of the principles discussed within the two fields overlap, there are some nuanced differences. By comparing and synthesizing the knowledge developed in them, we recommend an updated set of resilience-enhancing principles for infrastructure-dependent systems. In addition to proposing an expanded list of principles, we illustrate how these principles can co-occur and their interdependencies.     

Assessing Engineering Resilience for Systems with Multiple Performance Measures

Recently, efforts to model and assess a system's resilience to disruptions due to environmental and adversarial threats have increased substantially. Researchers have investigated resilience in many disciplines, including sociology, psychology, computer networks, and engineering systems, to name a few. When assessing engineering system resilience, the resilience assessment typically considers a single performance measure, a disruption, a loss of performance, the time required to recover, or a combination of these elements. We define and use a resilient engineered system definition that separates system resilience into platform and mission resilience. Most complex systems have multiple performance measures; this research proposes using multiple objective decision analysis to assess system resilience for systems with multiple performance measures using two distinct methods. The first method quantifies platform resilience and includes resilience and other “ilities” directly in the value hierarchy, while the second method quantifies mission resilience and uses the “ilities” in the calculation of the expected mission performance for every performance measure in the value hierarchy. We illustrate the mission resilience method using a transportation systems‐of‐systems network with varying levels of resilience due to the level of connectivity and autonomy of the vehicles and platform resilience by using a notional military example. Our analysis found that it is necessary to quantify performance in context with specific mission(s) and scenario(s) under specific threat(s) and then use modeling and simulation to help determine the resilience of a system for a given set of conditions. The example demonstrates how incorporating system mission resilience can improve performance for some performance measures while negatively affecting others.     

Systems Resilience for Multihazard Environments: Definition,Metrics, and Valuation for Decision Making

The United Nations Office for Disaster Risk Reduction reported that the 2011 natural disasters, including the earthquake and tsunami that struck Japan, resulted in $366 billion in direct damages and 29,782 fatalities worldwide. Storms and floods accounted for up to 70% of the 302 natural disasters worldwide in 2011, with earthquakes producing the greatest number of fatalities. Average annual losses in the United States amount to about $55 billion. Enhancing community and system resilience could lead to massive savings through risk reduction and expeditious recovery. The rational management of such reduction and recovery is facilitated by an appropriate definition of resilience and associated metrics. In this article, a resilience definition is provided that meets a set of requirements with clear relationships to the metrics of the relevant abstract notions of reliability and risk. Those metrics also meet logically consistent requirements drawn from measure theory, and provide a sound basis for the development of effective decision‐making tools for multihazard environments. Improving the resiliency of a system to meet target levels requires the examination of system enhancement alternatives in economic terms, within a decision‐making framework. Relevant decision analysis methods would typically require the examination of resilience based on its valuation by society at large. The article provides methods for valuation and benefit‐cost analysis based on concepts from risk analysis and management.     

Resilience of Cyber Systems with Over‐ and Underregulation

Recent cyber attacks provide evidence of increased threats to our critical systems and infrastructure. A common reaction to a new threat is to harden the system by adding new rules and regulations. As federal and state governments request new procedures to follow, each of their organizations implements their own cyber defense strategies. This unintentionally increases time and effort that employees spend on training and policy implementation and decreases the time and latitude to perform critical job functions, thus raising overall levels of stress. People's performance under stress, coupled with an overabundance of information, results in even more vulnerabilities for adversaries to exploit. In this article, we embed a simple regulatory model that accounts for cybersecurity human factors and an organization's regulatory environment in a model of a corporate cyber network under attack. The resulting model demonstrates the effect of under‐ and overregulation on an organization's resilience with respect to insider threats. Currently, there is a tendency to use ad‐hoc approaches to account for human factors rather than to incorporate them into cyber resilience modeling. It is clear that using a systematic approach utilizing behavioral science, which already exists in cyber resilience assessment, would provide a more holistic view for decisionmakers.     

Resilience Analysis of a Remote Offshore Oil and Gas Facility for a Potential Hydrocarbon Release

Resilience is the capability of a system to adjust its functionality during a disturbance or perturbation. The present work attempts to quantify resilience as a function of reliability, vulnerability, and maintainability. The approach assesses proactive and reactive defense mechanisms along with operational factors to respond to unwanted disturbances and perturbation. This article employs a Bayesian network format to build a resilience model. The application of the model is tested on hydrocarbon‐release scenarios during an offloading operation in a remote and harsh environment. The model identifies requirements for robust recovery and adaptability during an unplanned scenario related to a hydrocarbon release. This study attempts to relate the resilience capacity of a system to the system's absorptive, adaptive, and restorative capacities. These factors influence predisaster and postdisaster strategies that can be mapped to enhance the resilience of the system.     

Resilience Analysis of Countries under Disasters Based on Multisource Data

Disasters occur almost daily in the world. Because emergencies frequently have no precedent, are highly uncertain, and can be very destructive, improving a country's resilience is an efficient way to reduce risk. In this article, we collected more than 20,000 historical data points from disasters from 207 countries to enable us to calculate the severity of disasters and the danger they pose to countries. In addition, 6 primary indices (disaster, personal attribute, infrastructure, economics, education, and occupation) including 38 secondary influencing factors are considered in analyzing the resilience of countries. Using these data, we obtained the danger, expected number of deaths, and resilience of all 207 countries. We found that a country covering a large area is more likely to have a low resilience score. Through sensitivity analysis of all secondary indices, we found that population density, frequency of disasters, and GDP are the three most critical factors affecting resilience. Based on broad‐spectrum resilience analysis of the different continents, Oceania and South America have the highest resilience, while Asia has the lowest. Over the past 50 years, the resilience of many countries has been improved sharply, especially in developing countries. Based on our results, we analyze the comprehensive resilience and provide some optimal suggestions to efficiently improve resilience.     

Physical–cyber–human framework-based resilience evaluation toward urban power system: Case study from China

Because the increased frequency, intensity, and duration of extreme weather events have significantly challenged power systems, there has been an increased interest in resilient power systems. This article establishes a multicriteria resilience evaluation framework for urban power systems from a physical–cyber–human system perspective, in which the two principal elements responsible for power system function degradation are described, the three major domains comprising urban power systems are explained, four core capacities that positively contribute to power system resilience are proposed, and 15 (11 objective and four subjective) power system resilience evaluation indicators are identified. Fuzzy hesitant judgment and a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) aggregation method are employed to minimize the expert divergence and maximize the group consensus. A validation method is designed and a comparison with commonly applied performance-based and attributes-based evaluation methods is conducted. The applicability of the evaluation framework is verified using data from four Chinese municipalities: Shanghai, Beijing, Chongqing, and Tianjin. It was found that Shanghai's resilience was the best, and Chongqing's physical resistance disadvantages would result in the greatest difficulties in coping with extreme event disturbances. Physical, cyber, and human domain resilience enhancement strategies are given for different cities separately. This study provides a practical tool to evaluate, compare, and enhance power system resilience for governments and public utilities.     

Ein metrikbasierter Ansatz zur Messung der Aktualit?t von Daten in Informationssystemen

Due to the importance of using up-to-date data in information systems, this paper analyzes how the data quality dimension currency can be measured. Therefore, we design a probability based metric that allows for an objective and to a great extent automated assessment of data’s currency. In contrast to existing approaches, the resulting values of the new metric meet important requirements such as ratio scale and can be interpreted as probabilities. Hence, they can also be applied to calculate expected values for decision making in a methodically well-founded manner. Moreover, the metric can be adapted to the context of a particular application considering both, the specific characteristics of attribute values and supplemental data stored in the information system. The evaluation of the approach is based on six requirements for data quality metrics. Furthermore, the case of a mobile services provider illustrates the metric’s applicability and its practical benefit.     

On the Complex Definition of Risk: A Systems-Based Approach

The premise of this article is that risk to a system, as well as its vulnerability and resilience, can be understood, defined, and quantified most effectively through a systems-based philosophical and methodological approach, and by recognizing the central role of the system states in this process. A universally agreed-upon definition of risk has been difficult to develop; one reason is that the concept is multidimensional and nuanced. It requires an understanding that risk to a system is inherently and fundamentally a function of the initiating event, the states of the system and of its environment, and the time frame. In defining risk, this article posits that: (a) the performance capabilities of a system are a function of its state vector; (b) a system's vulnerability and resilience vectors are each a function of the input (e.g., initiating event), its time of occurrence, and the states of the system; (c) the consequences are a function of the specificity and time of the event, the vector of the states, the vulnerability, and the resilience of the system; (d) the states of a system are time-dependent and commonly fraught with variability uncertainties and knowledge uncertainties; and (e) risk is a measure of the probability and severity of consequences. The above implies that modeling must evaluate consequences for each risk scenario as functions of the threat (initiating event), the vulnerability and resilience of the system, and the time of the event. This fundamentally complex modeling and analysis process cannot be performed correctly and effectively without relying on the states of the system being studied.     

Understanding Community Resilience from a PRA Perspective Using Binary Decision Diagrams

Probabilistic risk assessment (PRA) is a useful tool to assess complex interconnected systems. This article leverages the capabilities of PRA tools developed for industrial and nuclear risk analysis in community resilience evaluations by modeling the food security of a community in terms of its built environment as an integrated system. To this end, we model the performance of Gilroy, CA, a moderate‐size town, with regard to disruptions in its food supply caused by a severe earthquake. The food retailers of Gilroy, along with the electrical power network, water network elements, and bridges are considered as components of a system. Fault and event trees are constructed to model the requirements for continuous food supply to community residents and are analyzed efficiently using binary decision diagrams (BDDs). The study also identifies shortcomings in approximate classical system analysis methods in assessing community resilience. Importance factors are utilized to rank the importance of various factors to the overall risk of food insecurity. Finally, the study considers the impact of various sources of uncertainties in the hazard modeling and performance of infrastructure on food security measures. The methodology can be applicable for any existing critical infrastructure system and has potential extensions to other hazards.     

Strategic and operational management of organizational resilience: Current state of research and future directions

This article uses both a systematic literature search and co-citation analysis to investigate the specific research domains of organizational resilience and its strategic and operational management to understand the current state of development and future research directions.The research stream on the organizational and operational management of resilience is distant from its infancy, but it can still be considered to be in a developing phase. We found evidence that the academic literature has reached a shared consensus on the definition of resilience, foundations, and characteristics and that in recent years, the main subfield of research has been supply chain resilience. Nevertheless, the literature is still far from reaching consensus on the implementation of resilience, i.e., how to reach operational resilience and how to create and maintain resilient processes. Finally, based on the results of in-depth co-citation and literature analysis, we found seven fruitful future research directions on strategic, organizational and operational resilience.     

Half Empty? Drinking Water Systems and Regional Resilience in Rural Canada

This paper investigates the extent that new regionalism has been employed in the planning of rural drinking water systems as a strategy to support rural regional resilience. Through a content analysis of policy documents, this paper explores the question of whether current approaches to the planning of rural drinking water systems reflect a transition towards regional resilience. While there is potential for drinking water systems to support or enhance rural regional resilience, this potential is emergent at best. Policy documents reveal the adoption of regionalist practices is challenged by a lack of regional cohesion and barriers from existing institutional structures.     

Sequential Hazards Resilience of Interdependent Infrastructure System: A Case Study of Greater Toronto Area Energy Infrastructure System

Coupled infrastructure systems and complicated multihazards result in a high level of complexity and make it difficult to assess and improve the infrastructure system resilience. With a case study of the Greater Toronto Area energy system (including electric, gas, and oil transmission networks), an approach to analysis of multihazard resilience of an interdependent infrastructure system is presented in the article. Integrating network theory, spatial and numerical analysis methods, the new approach deals with the complicated multihazard relations and complex infrastructure interdependencies as spatiotemporal impacts on infrastructure systems in order to assess the dynamic system resilience. The results confirm that the effects of sequential hazards on resilience of infrastructure (network) are more complicated than the sum of single hazards. The resilience depends on the magnitude of the hazards, their spatiotemporal relationship and dynamic combined impacts, and infrastructure interdependencies. The article presents a comparison between physical and functional resilience of an electric transmission network, and finds functional resilience is always higher than physical resilience. The multiple hazards resilience evaluation approach is applicable to any type of infrastructure and hazard and it can contribute to the improvement of infrastructure planning, design, and maintenance decision making.     

Murphy Scale: A locational equivalent intensity scale for hazard events

Empirical cross-hazard analysis and prediction of disaster vulnerability, resilience, and risk requires a common metric of hazard strengths across hazard types. In this paper, the authors propose an equivalent intensity scale for cross-hazard evaluation of hazard strengths of events for entire durations at locations. The proposed scale is called the Murphy Scale, after Professor Colleen Murphy. A systematic review and typology of hazard strength metrics is presented to facilitate the delineation of the defining dimensions of the proposed scale. An empirical methodology is introduced to derive equivalent intensities of hazard events on a Murphy Scale. Using historical data on impacts and hazard strength indicators of events from 2013 to 2017, the authors demonstrate the utility of the proposed methodology for computing the equivalent intensities for earthquakes and tropical cyclones. As part of a new area of research called hazard equivalency, the proposed Murphy Scale paves the way toward creating multi-hazard hazard maps. The proposed scale can also be leveraged to facilitate hazard communication regarding past and future local experiences of hazard events for enhancing multi-hazard preparedness, mitigation, and emergency response.     

Workforce/Population,Economy, Infrastructure,Geography, Hierarchy,and Time (WEIGHT): Reflections on the Plural Dimensions of Disaster Resilience

The concept of resilience and its relevance to disaster risk management has increasingly gained attention in recent years. It is common for risk and resilience studies to model system recovery by analyzing a single or aggregated measure of performance, such as economic output or system functionality. However, the history of past disasters and recent risk literature suggest that a single-dimension view of relevant systems is not only insufficient, but can compromise the ability to manage risk for these systems. In this article, we explore how multiple dimensions influence the ability for complex systems to function and effectively recover after a disaster. In particular, we compile evidence from the many competing resilience perspectives to identify the most critical resilience dimensions across several academic disciplines, applications, and disaster events. The findings demonstrate the need for a conceptual framework that decomposes resilience into six primary dimensions: workforce/population, economy, infrastructure, geography, hierarchy, and time (WEIGHT). These dimensions are not typically addressed holistically in the literature; often they are either modeled independently or in piecemeal combinations. The current research is the first to provide a comprehensive discussion of each resilience dimension and discuss how these dimensions can be integrated into a cohesive framework, suggesting that no single dimension is sufficient for a holistic analysis of a disaster risk management. Through this article, we also aim to spark discussions among researchers and policymakers to develop a multicriteria decision framework for evaluating the efficacy of resilience strategies. Furthermore, the WEIGHT dimensions may also be used to motivate the generation of new approaches for data analytics of resilience-related knowledge bases.