基于知识元的突发事件风险分析

科学地分析突发事件的风险,有利于应急管理部门正确制定应对方案,降低事件损失。突发事件风险分析中受到多因素高维数据和小样本数据信息不完备的约束,无法全面识别突发事件的风险。本文从突发事件系统观点出发,以知识元模型、投影寻踪方法和信息扩散理论为基础,提出了基于知识元的突发事件风险分析方法。该方法采用知识元模型描述了突发事件已认知的共性本体特征,通过探寻事件风险等级标准数据的最佳投影方向降低了输入元素观测数据的维数,将输入元素观测样本所包含的风险信息扩散到输出属性的风险指标论域的控制点上,从而确定了突发事件发生的风险概率。实例分析中,根据国家《地表水环境质量标准（GB3838-2002）》划分水污染风险等级,利用某湖泊8个监测点实时检测数据,分析该湖泊突发水污染事件的风险性。研究结果表明基于知识元的突发事件风险分析方法能够根据研究区域突发事件风险等级标准和观测点的样本数据,动态定量的分析和评估突发事件潜在风险,为突发事件的应急管理提供科学依据。本文提出的突发事件风险方法对于已经建立实时监测系统的危险区域分析突发事件的风险性具有一定的借鉴意义。     

Towards a Fuzzy Bayesian Network Based Approach for Safety Risk Analysis of Tunnel‐Induced Pipeline Damage

Tunneling excavation is bound to produce significant disturbances to surrounding environments, and the tunnel‐induced damage to adjacent underground buried pipelines is of considerable importance for geotechnical practice. A fuzzy Bayesian networks (FBNs) based approach for safety risk analysis is developed in this article with detailed step‐by‐step procedures, consisting of risk mechanism analysis, the FBN model establishment, fuzzification, FBN‐based inference, defuzzification, and decision making. In accordance with the failure mechanism analysis, a tunnel‐induced pipeline damage model is proposed to reveal the cause‐effect relationships between the pipeline damage and its influential variables. In terms of the fuzzification process, an expert confidence indicator is proposed to reveal the reliability of the data when determining the fuzzy probability of occurrence of basic events, with both the judgment ability level and the subjectivity reliability level taken into account. By means of the fuzzy Bayesian inference, the approach proposed in this article is capable of calculating the probability distribution of potential safety risks and identifying the most likely potential causes of accidents under both prior knowledge and given evidence circumstances. A case concerning the safety analysis of underground buried pipelines adjacent to the construction of the Wuhan Yangtze River Tunnel is presented. The results demonstrate the feasibility of the proposed FBN approach and its application potential. The proposed approach can be used as a decision tool to provide support for safety assurance and management in tunnel construction, and thus increase the likelihood of a successful project in a complex project environment.     

Three‐Stage Decision‐Making Model under Restricted Conditions for Emergency Response to Ships Not under Control

A ship that is not under control (NUC) is a typical incident that poses serious problems when in confined waters close to shore. The emergency response to NUC ships is to select the best risk control options, which is a challenge in restricted conditions (e.g., time limitation, resource constraint, and information asymmetry), particularly in inland waterway transportation. To enable a quick and effective response, this article develops a three‐stage decision‐making framework for NUC ship handling. The core of this method is (1) to propose feasible options for each involved entity (e.g., maritime safety administration, NUC ship, and ships passing by) under resource constraint in the first stage, (2) to select the most feasible options by comparing the similarity of the new case and existing cases in the second stage, and (3) to make decisions considering the cooperation between the involved organizations by using a developed Bayesian network in the third stage. Consequently, this work provides a useful tool to achieve well‐organized management of NUC ships.     

Multiattribute Risk Analysis in Nuclear Emergency Management

Radiation protection authorities have seen a potential for applying multiattribute risk analysis in nuclear emergency management and planning to deal with conflicting objectives, different parties involved, and uncertainties. This type of approach is expected to help in the following areas: to ensure that all relevant attributes are considered in decision making; to enhance communication between the concerned parties, including the public; and to provide a method for explicitly including risk analysis in the process. A multiattribute utility theory analysis was used to select a strategy for protecting the population after a simulated nuclear accident. The value-focused approach and the use of a neutral facilitator were identified as being useful.     

Risk Management of Domino Effects Considering Dynamic Consequence Analysis

Domino effects are low‐probability high‐consequence accidents causing severe damage to humans, process plants, and the environment. Because domino effects affect large areas and are difficult to control, preventive safety measures have been given priority over mitigative measures. As a result, safety distances and safety inventories have been used as preventive safety measures to reduce the escalation probability of domino effects. However, these safety measures are usually designed considering static accident scenarios. In this study, we show that compared to a static worst‐case accident analysis, a dynamic consequence analysis provides a more rational approach for risk assessment and management of domino effects. This study also presents the application of Bayesian networks and conflict analysis to risk‐based allocation of chemical inventories to minimize the consequences and thus to reduce the escalation probability. It emphasizes the risk management of chemical inventories as an inherent safety measure, particularly in existing process plants where the applicability of other safety measures such as safety distances is limited.     

基于元胞自动机的企业集团信用风险传递仿真

 企业集团作为商业银行的重要客户,其内部信用风险传递机制的仿真与预测研究对于商业银行的风险控制尤其重要。而传统的仿真方法或者没有考虑到企业集团其本身的结构特性,或者过分依赖于具体随机过程的假定,大大降低了其结论的可靠性。因此,本文引入在生物与计算机领域广泛使用的元胞模型对企业集团内部关联信用风险传递机制进行仿真与模拟。研究发现,企业集团中的信用风险传递会出现风险分散和风险扩大两种不同的效应,并且母公司可以选择对各子公司的最优控制度使得母公司的违约概率最低。     

Domino Effect Analysis Using Bayesian Networks

A new methodology is introduced based on Bayesian network both to model domino effect propagation patterns and to estimate the domino effect probability at different levels. The flexible structure and the unique modeling techniques offered by Bayesian network make it possible to analyze domino effects through a probabilistic framework, considering synergistic effects, noisy probabilities, and common cause failures. Further, the uncertainties and the complex interactions among the domino effect components are captured using Bayesian network. The probabilities of events are updated in the light of new information, and the most probable path of the domino effect is determined on the basis of the new data gathered. This study shows how probability updating helps to update the domino effect model either qualitatively or quantitatively. The methodology is applied to a hypothetical example and also to an earlier‐studied case study. These examples accentuate the effectiveness of Bayesian network in modeling domino effects in processing facility.     

GIS‐Based Integration of Social Vulnerability and Level 3 Probabilistic Risk Assessment to Advance Emergency Preparedness,Planning, and Response for Severe Nuclear Power Plant Accidents

In the nuclear power industry, Level 3 probabilistic risk assessment (PRA) is used to estimate damage to public health and the environment if a severe accident leads to large radiological release. Current Level 3 PRA does not have an explicit inclusion of social factors and, therefore, it is not possible to perform importance ranking of social factors for risk‐informing emergency preparedness, planning, and response (EPPR). This article offers a methodology for adapting the concept of social vulnerability, commonly used in natural hazard research, in the context of a severe nuclear power plant accident. The methodology has four steps: (1) calculating a hazard‐independent social vulnerability index for the local population; (2) developing a location‐specific representation of the maximum radiological hazard estimated from current Level 3 PRA, in a geographic information system (GIS) environment; (3) developing a GIS‐based socio‐technical risk map by combining the social vulnerability index and the location‐specific radiological hazard; and (4) conducting a risk importance measure analysis to rank the criticality of social factors based on their contribution to the socio‐technical risk. The methodology is applied using results from the 2012 Surry Power Station state‐of‐the‐art reactor consequence analysis. A radiological hazard model is generated from MELCOR accident consequence code system, translated into a GIS environment, and combined with the Center for Disease Control social vulnerability index (SVI). This research creates an opportunity to explicitly consider and rank the criticality of location‐specific SVI themes based on their influence on risk, providing input for EPPR.     

Context‐Specific,Scenario‐Based Risk Scales

Reacting to an emergency requires quick decisions under stressful and dynamic conditions. To react effectively, responders need to know the right actions to take given the risks posed by the emergency. While existing research on risk scales focuses primarily on decision making in static environments with known risks, these scales may be inappropriate for conditions where the decision maker's time and mental resources are limited and may be infeasible if the actual risk probabilities are unknown. In this article, we propose a method to develop context‐specific, scenario‐based risk scales designed for emergency response training. Emergency scenarios are used as scale points, reducing our dependence on known probabilities; these are drawn from the targeted emergency context, reducing the mental resources required to interpret the scale. The scale is developed by asking trainers/trainees to rank order a range of risk scenarios and then aggregating these orderings using a Kemeny ranking. We propose measures to assess this aggregated scale's internal consistency, reliability, and validity, and we discuss how to use the scale effectively. We demonstrate our process by developing a risk scale for subsurface coal mine emergencies and test the reliability of the scale by repeating the process, with some methodological variations, several months later.     

Adversarial Risk Analysis with Incomplete Information: A Level‐k Approach

This article proposes, develops, and illustrates the application of level‐k game theory to adversarial risk analysis. Level‐k reasoning, which assumes that players play strategically but have bounded rationality, is useful for operationalizing a Bayesian approach to adversarial risk analysis. It can be applied in a broad class of settings, including settings with asynchronous play and partial but incomplete revelation of early moves. Its computational and elicitation requirements are modest. We illustrate the approach with an application to a simple defend‐attack model in which the defender's countermeasures are revealed with a probability less than one to the attacker before he decides on how or whether to attack.     

Tree‐Based Logistic Regression Approach for Work Zone Casualty Risk Assessment

This study presents a tree‐based logistic regression approach to assessing work zone casualty risk, which is defined as the probability of a vehicle occupant being killed or injured in a work zone crash. First, a decision tree approach is employed to determine the tree structure and interacting factors. Based on the Michigan M‐94I‐94I‐94BLI‐94BR highway work zone crash data, an optimal tree comprising four leaf nodes is first determined and the interacting factors are found to be airbag, occupant identity (i.e., driver, passenger), and gender. The data are then split into four groups according to the tree structure. Finally, the logistic regression analysis is separately conducted for each group. The results show that the proposed approach outperforms the pure decision tree model because the former has the capability of examining the marginal effects of risk factors. Compared with the pure logistic regression method, the proposed approach avoids the variable interaction effects so that it significantly improves the prediction accuracy.     

Integrating Household Risk Mitigation Behavior in Flood Risk Analysis: An Agent‐Based Model Approach

Recent studies showed that climate change and socioeconomic trends are expected to increase flood risks in many regions. However, in these studies, human behavior is commonly assumed to be constant, which neglects interaction and feedback loops between human and environmental systems. This neglect of human adaptation leads to a misrepresentation of flood risk. This article presents an agent‐based model that incorporates human decision making in flood risk analysis. In particular, household investments in loss‐reducing measures are examined under three economic decision models: (1) expected utility theory, which is the traditional economic model of rational agents; (2) prospect theory, which takes account of bounded rationality; and (3) a prospect theory model, which accounts for changing risk perceptions and social interactions through a process of Bayesian updating. We show that neglecting human behavior in flood risk assessment studies can result in a considerable misestimation of future flood risk, which is in our case study an overestimation of a factor two. Furthermore, we show how behavior models can support flood risk analysis under different behavioral assumptions, illustrating the need to include the dynamic adaptive human behavior of, for instance, households, insurers, and governments. The method presented here provides a solid basis for exploring human behavior and the resulting flood risk with respect to low‐probability/high‐impact risks.     

A Reliability‐Based Capability Approach

This article proposes a rigorous mathematical approach, named a reliability‐based capability approach (RCA), to quantify the societal impact of a hazard. The starting point of the RCA is a capability approach in which capabilities refer to the genuine opportunities open to individuals to achieve valuable doings and beings (such as being mobile and being sheltered) called functionings. Capabilities depend on what individuals have and what they can do with what they have. The article develops probabilistic predictive models that relate the value of each functioning to a set of easily predictable or measurable quantities (regressors) in the aftermath of a hazard. The predicted values of selected functionings for an individual collectively determine the impact of a hazard on his/her state of well‐being. The proposed RCA integrates the predictive models of functionings into a system reliability problem to determine the probability that the state of well‐being is acceptable, tolerable, or intolerable. Importance measures are defined to quantify the contribution of each functioning to the state of well‐being. The information from the importance measures can inform decisions on optimal allocation of limited resources for risk mitigation and management.     

A Bayesian Semiparametric Model for Radiation Dose‐Response Estimation

In evaluating the risk of exposure to health hazards, characterizing the dose‐response relationship and estimating acceptable exposure levels are the primary goals. In analyses of health risks associated with exposure to ionizing radiation, while there is a clear agreement that moderate to high radiation doses cause harmful effects in humans, little has been known about the possible biological effects at low doses, for example, below 0.1 Gy, which is the dose range relevant to most radiation exposures of concern today. A conventional approach to radiation dose‐response estimation based on simple parametric forms, such as the linear nonthreshold model, can be misleading in evaluating the risk and, in particular, its uncertainty at low doses. As an alternative approach, we consider a Bayesian semiparametric model that has a connected piece‐wise‐linear dose‐response function with prior distributions having an autoregressive structure among the random slope coefficients defined over closely spaced dose categories. With a simulation study and application to analysis of cancer incidence data among Japanese atomic bomb survivors, we show that this approach can produce smooth and flexible dose‐response estimation while reasonably handling the risk uncertainty at low doses and elsewhere. With relatively few assumptions and modeling options to be made by the analyst, the method can be particularly useful in assessing risks associated with low‐dose radiation exposures.     

AbSRiM: An Agent‐Based Security Risk Management Approach for Airport Operations

Security risk management is essential for ensuring effective airport operations. This article introduces AbSRiM, a novel agent‐based modeling and simulation approach to perform security risk management for airport operations that uses formal sociotechnical models that include temporal and spatial aspects. The approach contains four main steps: scope selection, agent‐based model definition, risk assessment, and risk mitigation. The approach is based on traditional security risk management methodologies, but uses agent‐based modeling and Monte Carlo simulation at its core. Agent‐based modeling is used to model threat scenarios, and Monte Carlo simulations are then performed with this model to estimate security risks. The use of the AbSRiM approach is demonstrated with an illustrative case study. This case study includes a threat scenario in which an adversary attacks an airport terminal with an improvised explosive device. The approach provides a promising way to include important elements, such as human aspects and spatiotemporal aspects, in the assessment of risk. More research is still needed to better identify the strengths and weaknesses of the AbSRiM approach in different case studies, but results demonstrate the feasibility of the approach and its potential.     

A Probabilistic Framework for Risk Analysis of Widespread Flood Events: A Proof‐of‐Concept Study

This article presents a flood risk analysis model that considers the spatially heterogeneous nature of flood events. The basic concept of this approach is to generate a large sample of flood events that can be regarded as temporal extrapolation of flood events. These are combined with cumulative flood impact indicators, such as building damages, to finally derive time series of damages for risk estimation. Therefore, a multivariate modeling procedure that is able to take into account the spatial characteristics of flooding, the regionalization method top‐kriging, and three different impact indicators are combined in a model chain. Eventually, the expected annual flood impact (e.g., expected annual damages) and the flood impact associated with a low probability of occurrence are determined for a study area. The risk model has the potential to augment the understanding of flood risk in a region and thereby contribute to enhanced risk management of, for example, risk analysts and policymakers or insurance companies. The modeling framework was successfully applied in a proof‐of‐concept exercise in Vorarlberg (Austria). The results of the case study show that risk analysis has to be based on spatially heterogeneous flood events in order to estimate flood risk adequately.     

QRA Model‐Based Risk Impact Analysis of Traffic Flow in Urban Road Tunnels

Road tunnels are vital infrastructures providing underground vehicular passageways for commuters and motorists. Various quantitative risk assessment (QRA) models have recently been developed and employed to evaluate the safety levels of road tunnels in terms of societal risk (as measured by the F/N curve). For a particular road tunnel, traffic volume and proportion of heavy goods vehicles (HGVs) are two adjustable parameters that may significantly affect the societal risk, and are thus very useful in implementing risk reduction solutions. To evaluate the impact the two contributing factors have on the risk, this article first presents an approach that employs a QRA model to generate societal risk for a series of possible combinations of the two factors. Some combinations may result in F/N curves that do not fulfill a predetermined safety target. This article thus proposes an “excess risk index” in order to quantify the road tunnel risk magnitudes that do not pass the safety target. The two‐factor impact analysis can be illustrated by a contour chart based on the excess risk. Finally, the methodology has been applied to Singapore's KPE road tunnel and the results show that in terms of meeting the test safety target for societal risk, the traffic capacity of the tunnel should be no more than 1,200 vehs/h/lane, with a maximum proportion of 18% HGVs.     

Risk Communication Emergency Response Preparedness: Contextual Assessment of the Protective Action Decision Model

Studies are continuously performed to improve risk communication campaign designs to better prepare residents to act in the safest manner during an emergency. To that end, this article investigates the predictive ability of the protective action decision model (PADM), which links environmental and social cues, predecision processes (attention, exposure, and comprehension), and risk decision perceptions (threat, alternative protective actions, and stakeholder norms) with protective action decision making. This current quasi‐longitudinal study of residents (N = 400 for each year) in a high‐risk (chemical release) petrochemical manufacturing community investigated whether PADM core risk perceptions predict protective action decision making. Telephone survey data collected at four intervals (1995, 1998, 2002, 2012) reveal that perceptions of protective actions and stakeholder norms, but not of threat, currently predict protective action decision making (intention to shelter in place). Of significance, rather than threat perceptions, perception of Wally Wise Guy (a spokes‐character who advocates shelter in place) correlates with perceptions of protective action, stakeholder norms, and protective action decision making. Wally's response‐efficacy advice predicts residents’ behavioral intentions to shelter in place, thereby offering contextually sensitive support and refinement for PADM.     

Comparison of Risk Predicted by Multiple Norovirus Dose–Response Models and Implications for Quantitative Microbial Risk Assessment

The application of quantitative microbial risk assessments (QMRAs) to understand and mitigate risks associated with norovirus is increasingly common as there is a high frequency of outbreaks worldwide. A key component of QMRA is the dose–response analysis, which is the mathematical characterization of the association between dose and outcome. For Norovirus, multiple dose–response models are available that assume either a disaggregated or an aggregated intake dose. This work reviewed the dose–response models currently used in QMRA, and compared predicted risks from waterborne exposures (recreational and drinking) using all available dose–response models. The results found that the majority of published QMRAs of norovirus use the ₁F₁ hypergeometric dose–response model with α = 0.04, β = 0.055. This dose–response model predicted relatively high risk estimates compared to other dose–response models for doses in the range of 1–1,000 genomic equivalent copies. The difference in predicted risk among dose–response models was largest for small doses, which has implications for drinking water QMRAs where the concentration of norovirus is low. Based on the review, a set of best practices was proposed to encourage the careful consideration and reporting of important assumptions in the selection and use of dose–response models in QMRA of norovirus. Finally, in the absence of one best norovirus dose–response model, multiple models should be used to provide a range of predicted outcomes for probability of infection.     

Animal and Human Dose‐Response Models for Brucella Species

Human Brucellosis is one of the most common zoonotic diseases worldwide. Disease transmission often occurs through the handling of domestic livestock, as well as ingestion of unpasteurized milk and cheese, but can have enhanced infectivity if aerosolized. Because there is no human vaccine available, rising concerns about the threat of Brucellosis to human health and its inclusion in the Center for Disease Control's Category B Bioterrorism/Select Agent List make a better understanding of the dose‐response relationship of this microbe necessary. Through an extensive peer‐reviewed literature search, candidate dose‐response data were appraised so as to surpass certain standards for quality. The statistical programming language, “R,” was used to compute the maximum likelihood estimation to fit two models, the exponential and the approximate beta‐Poisson (widely used for quantitative risk assessment) to dose‐response data. Dose‐response models were generated for prevalent species of Brucella: Br. suis, Br. melitensis, and Br. abortus. Dose‐response models were created for aerosolized Br. suis exposure to guinea pigs from pooled studies. A parallel model for guinea pigs inoculated through both aerosol and subcutaneous routes with Br. melitensis showed that the median infectious dose corresponded to a 30 colony‐forming units (CFU) dose of Br. suis, much less than the N₅₀ dose of about 94 CFU for Br. melitensis organisms. When Br. melitensis was tested subcutaneously on mice, the N₅₀ dose was higher, 1,840 CFU. A dose‐response model was constructed from pooled data for mice, rhesus macaques, and humans inoculated through three routes (subcutaneously/aerosol/intradermally) with Br. melitensis.