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Highlights: [•] Theoretically placing strategic communication management within the DRR paradigm. [•] Defining DR4 in the South African environment. [•] Indicating the need for DR4 communication in the South African environment. [Copyright &y& Elsevier]

Emergency decision-making is vital for nations or communities because it increases emergency management's effectiveness and legitimacy, which in turn greatly minimizes environmental damage, fatalities, and economic loss. The evaluation of emergency judgements must take into account significant inaccuracy, fuzzyness, and ambiguity. While high risk and uncertainty are frequently characteristics of emergency decision-making (EDM) circumstances. The current EDM methodologies do not take into account the psychological behavior of the decision makers in addition to the various emergency situations and the various responses. In this paper, we presented a new method based on a Complex Probabilistic Hesitant Fuzzy Soft Set (CPHFSS) as represented by membership in 2-D with hesitant probability. The idea that is being put forth captures the ambiguity and takes into account emergency situations while also taking into account the psychological state of the decision-makers involved in the EDM procedure. The introduction of a new score function for the CPHFSS serves as the second objective of this paper, which is to address a number of comparability issues. We examine a novel family of hybrid operators for CPHFSS that utilize numerous independent variables as a solution to these unforeseen issues. The group decision-making strategy and EDAS technique are then suggested employing these operators. Later, an emergency decision-making situation involving a major fire in China is used to demonstrate the validity of the algorithms. • Concept of complex probabilistic hesitant fuzzy soft set is presented. • Introduce a new score function for the CPHFSS. • Group decision-making strategy and EDAS technique presented. • Emergency decision-making problems discussed in the study. • Case study related to industrial disaster emergency decision-making discuss. [ABSTRACT FROM AUTHOR]

In the decision-making process, probabilistic linguistic term sets (PLTSs) could represent decision-makers' uncertain evaluation information. The combination of probabilistic and linguistic information assists decision-makers to depict their evaluation information more flexibly. In this paper, a prospect outranking decision-making method based on stochastic dominance is proposed for solving the multi-attribute risk decision-making problem under the probabilistic linguistic information environment. Firstly, to fully preserve the original probabilistic linguistic information, we give the definitions of the generalized probability distribution and the corresponding generalized cumulative distribution function of a PLTS for defining the distance measure between PLTSs. Secondly, based on the positive and negative ideal cumulative distribution functions, we propose the probabilistic linguistic prospect value functions. After that, considering the uncertainty of the relationship between alternatives, several kinds of novel prospect stochastic dominance and almost stochastic dominance relations are defined, which consider the decision makers' distinction between gains and losses to measure the subtle relationship between alternatives. The stochastic dominance approach and the almost stochastic dominance approach under the probabilistic linguistic decision-making environment are put forward. Besides, we use the superiority and inferiority ranking method to solve the dominance ranking problem of the uncertainty relationship between any two alternatives. Finally, the proposed method is applied in the evaluation of emergency plans to show the accuracy and reliability, and comparative analyses are made to illustrate its availability. [ABSTRACT FROM AUTHOR]

The paper proposes a new information set of multiple interacting fuzzy linguistic set(MIFLS) to describe the interaction of uncertain linguistic information. The property of the MIFLS and its characteristic of processing consensus information are also discussed, the introduction of MIFLS provides a new perspective to improve the consistency of decision makers' evaluation information, and the application of MIFLS can improve the accuracy of decision making. Furthermore, we propose an optimization consistency information model based on the MIFLS and TOPSIS method, an example of emergency decision plan is used to illustrate the applicability and efficiency of the proposed model. The optimal consistency adjustment is not only the highly consistent evaluation information, but also the minimal difference between the original information and the adjusted information. The proposed MIFLS is fit for dealing with the interaction of decision information, which means MIFLS is one of the effective tools to processing the interaction of uncertain fuzzy set and dealing with the emergency decision making problem. More important, the MIFLS not only provides a way to quantify the correlation of multi-attribute information, but also provides a method how to improve the consistency of decision-maker information. [ABSTRACT FROM AUTHOR]

This paper presents a new facility location problem variant with application in disaster relief. The problem is unique in that both verified data and unverified user-generated data are available for consideration during decision making. The problem is motivated by the recent need of integrating unverified social data (e.g., Twitter posts) with data from more traditional sources, such as on-the-ground assessments and aerial flyovers, to make optimal decisions during disaster relief. Integrating social data can enable identifying larger numbers of needs in shorter amounts of time, but because the information is unverified, some of it may be inaccurate. This paper seeks to provide a “proof of concept” illustrating how the unverified social data may be exploited. To do so, a framework for incorporating uncertain user-generated data when locating Points of Distribution (PODs) for disaster relief is presented. Then, three decision strategies that differ in how the uncertain data is considered are defined. Finally, the framework and decision strategies are demonstrated via a small computational study to illustrate the benefits user-generated data may afford across a variety of disaster scenarios. [ABSTRACT FROM AUTHOR]

Decision-makers, managers and communities in low-lying coastal regions face many challenges in planning for and adapting to escalating coastal hazard risk, given uncertainty about sea-level rise and complex and contested environmental and socio-economic futures. Collaborative and participatory approaches can help to address such challenges and enable proactive adaptation. However, there is limited empirical evidence of such approaches yielding outcomes that can genuinely be described as shifting business as usual, and fewer still that have informed institutional change. This paper provides such evidence based on an assessment of a novel collaboration between local government, Māori, stakeholders, community members, consultants and researchers in the formulation of a 100-year coastal hazard management strategy in Aotearoa New Zealand. Thematic analysis of interviews, surveys and workshop materials was used to distil lessons learned by participants involved in formulating this anticipatory long-term strategy. Results indicated the importance of deliberative, flexible and transparent governance processes that can enable collaboration amongst Māori, local and region-wide stakeholders, in a process consistent with and supported by the national governmental frameworks, and regulatory and non-regulatory measures. The importance of aligning scientific, local and Indigenous knowledges is highlighted. Furthermore, novel tools and methodologies were used in the strategy formulation process, hitherto not applied in a real-life coastal decision-making process, to address changing risk over time and uncertainties. This enabled the tailoring of strategic planning and decision processes to reflect local contexts and governance interactions. Adaptive pathways were developed to enable short-term actions to be taken while leaving open long-term options and alternative pathways available for future adjustment as the hazards and impacts intensify. The lessons learned from the development of the coastal management strategy offer insights to support future collaborative decision-making processes in climate change adaptation. They advance scholarly understanding about how sea-level rise risks can be addressed in a long-term strategic formulation process in a dynamic and 'fit-for-purpose' manner. [Display omitted] • We thematically analysed the formulation of a 100-year coastal hazards strategy. • Key themes emerged to reveal lessons learned from the process. • Collaborative and flexible governance processes contributed to building trust. • Proactive, tailored decision tools enabled development of adaptation pathways. • Scientific, technical, local and Indigenous knowledges were incorporated. [ABSTRACT FROM AUTHOR]

Timely knowledge sharing in disaster management (DM) is clearly vital, but it is remains challenging. Roles involved in DM processes often cut across many organizational boundaries and are dynamic. Knowledge involved is enormous and diverse. It includes information related to varieties of disasters, roles descriptions, plans and operations. Alas, practices may also vary across different regions and authorities. This paper makes a crucial contribution to address the knowledge sharing challenge by providing a knowledge based systems approach to facilitate structuring, storing and reusing DM knowledge. The contribution of this paper is three folds: Firstly, it presents a metamodel-based architecture suitable for various distributed knowledge sharing settings; Secondly, it presents an actual implementation of such a system, the Disaster Management Knowledge Repository (DMKR1.0). DMKR facilitates collaboration and DM knowledge sharing using a tailored DM language. This offers a flexible structure to allow the storage and retrieval not only of observed and measured data, but also interpretative and inferred information of the disaster management knowledge. Thirdly, the paper provides disaster management exemplars of how DMKR users can easily instantiate DM models to communicate and to generalize their knowledge for the benefit of sharing it within their community. This presents a compelling evidence of the soundness and the effectiveness of the overall approach to DM knowledge sharing. [ABSTRACT FROM AUTHOR]

This paper demonstrates that transportation networks may be used to assess and predict the regional risk of COVID-19 infection from the outbreak. We use China's high-speed rail (HSR) network at the scale of prefecture level to assess, based on a probabilistic risk model, the risk of COVID-19 infection from Wuhan to the country's 31 province-level regions at the early stage of domestic spread. We find that the high-risk regions are mainly distributed along the southern half of Beijing-Hong Kong HSR line, where a large number of infection cases have been confirmed at the early stage. Furthermore, the two components of the infection risk, namely, the probability (proxied by the region's correlation with Wuhan through HSR) and the impact (proxied by the region's population with mobility), can play different roles in the risk ranking for different regions. For public health administrators, these findings may be used for better decision making, including the preparation of emergency plans and supplies, and the allocation of limited resources, before the extensive spread of the epidemic. Moreover, the administrators should adopt different intervention measures for different regions, so as to better mitigate the epidemic spread according to their own risk scenarios with respect to the probability of occurring and, once occurred, the impact. [ABSTRACT FROM AUTHOR]

Accurate identification of the spatial and temporal dynamic impacts of disasters on road networks is crucial for making informed decisions regarding postdisaster emergency response and recovery. In this paper, we propose a multidimensional vulnerability assessment framework that utilizes time series crowdsourced data. The framework enables a spatiotemporal analysis of traffic flows at the link level, allowing us to identify the primary factors that contribute to vulnerability after a disaster. The results show that there is a significant variation in link vulnerability over time, with the overall network gradually returning to a normal state. We also observe a global correlation in link vulnerability across the spatial aspect, although the autocorrelation effect is only present among directly contiguous links. Furthermore, metrics affecting link vulnerability varied during different periods of response. Graph-based metrics of road network structure, population density, and road type are key factors influencing postearthquake vulnerability. [ABSTRACT FROM AUTHOR]

Human Behaviour during emergency situations is a crucial component of any response. The ability of responders to effectively engage with casualties is critical to ensuring that any instructions given are followed and in doing assist rather than hinder the response. In order to improve the likelihood of this occurring it is essential to understand what drives decision making during emergencies in order to be able to effectively influence these. This paper will seek to establish what these behaviours are likely to be and what is likely to influence these in order to inform responder tactics and training. This paper seeks to develop a psychological model of casualty behaviour during a hazardous materials evacuation. The study utilises a survey of members of the public evacuated from their homes or places of work due to a fire impacting an ammonia tank in February 2019 at the Ocado distribution warehouse in Andover. The results of this survey were used to validate a hypothesised psychological model utilising Path Analysis. The research identifies the importance of recognising the ability of casualties involved in emergency situations to remain rational and utilise information and instructions given to them. The paper highlights the importance which trust plays in engaging with casualties in order to provide effective information and instructions and how trust is constructed of both legitimacy and competency and influenced by the communications of responders. Most crucially the paper identified how trust during an emergency situation is the key driver of whether casualties are likely to co-operate with instructions and emergency responders. The research utilised real world data to validate findings demonstrating the need for emergency responders to effectively engage with casualties and has implications for both guidance and training of emergency responders in managing casualties. [ABSTRACT FROM AUTHOR]

Inquiries into the causes of man-made disasters have shown repeatedly that ongoing safety of workers and the public depends on decisions made, often many years earlier, during the design stage of infrastructure or facility development. The potential for disaster represented by incompatibilities that remain undiscovered between design choices and future operating requirements is a classic example of what Turner calls ‘disaster incubation’ – where beliefs about control of hazards are at odds with actual events. This research investigates the motivations and attitudes of people working in a design office environment who determine the form of hazardous facilities such as oil refineries, petrochemical plants and pipelines. It explores particularly the issue of responsibility for public safety. This long-term goal faces serious competition for attention from short-term project priorities of integrity, cost and schedule. Linking requirements for public safety to facility integrity (as is often done in a project environment) is shown to have substantial limitations as a strategy for ensuring the best long term outcomes. As a counter to this, some senior discipline engineers were found to emphasise the real-world potential consequences of their work through use of stories and saw communication of professional values to younger staff as a key aspect of their work. The paper also touches on the perceived role of engineering standards and shows that safety design is seen to go beyond simply compliance. There has been little social science research in this important area. The paper concludes with areas in which further work would be valuable. [ABSTRACT FROM AUTHOR]

To meet the urgent needs of emergency observation missions, a well-designed constellation is key to ensuring system performance. This paper presents a multi-objective optimization framework which is well suited for constellation designers to identify key trade-offs and make decisions. Firstly, this work investigates the metrics of responsiveness and coverage simultaneously to approximate to the actual conditions of emergencies, and a multi-objective mathematical model is constructed to explore solutions to enhance the overall system performance. Then, according to the characteristics of emergency missions, a hierarchical chromosome encoding method is proposed in the framework to encode a solution that explores different sized constellations without constraining its specific geometry. Finally, we propose the use of radial axes plots to help constellation designers to gain pragmatic insights for informed decision making. The simulation of disaster management verifies that the proposed chromosome encoding method effectively promotes the convergence of the algorithm, and the multi-objective optimization framework helps to generate a rapid and adequate observation satellite constellation to provide maximum coverage performance while ensuring timeliness. [ABSTRACT FROM AUTHOR]

• Timelines of wildland-urban interface (WUI) fire events are studied. • A multi-physics modelling framework for WUI fire has been developed. • The key features for system integration are identified and listed. Fire evacuations at wildland-urban interfaces (WUI) pose a serious challenge to the emergency services, and are a global issue affecting thousands of communities around the world. This paper presents a multi-physics framework for the simulation of evacuation in WUI wildfire incidents, including three main modelling layers: wildfire, pedestrians, and traffic. Currently, these layers have been mostly modelled in isolation and there is no comprehensive model which accounts for their integration. The key features needed for system integration are identified, namely: consistent level of refinement of each layer (i.e. spatial and temporal scales) and their application (e.g. evacuation planning or emergency response), and complete data exchange. Timelines of WUI fire events are analysed using an approach similar to building fire engineering (available vs. required safe egress times for WUI fires, i.e. WASET/WRSET). The proposed framework allows for a paradigm shift from current wildfire risk assessment and mapping tools towards dynamic fire vulnerability mapping. This is the assessment of spatial and temporal vulnerabilities based on the wildfire threat evolution along with variables related to the infrastructure, population and network characteristics. This framework allows for the integration of the three main modelling layers affecting WUI fire evacuation and aims at improving the safety of WUI communities by minimising the consequences of wildfire evacuations. [ABSTRACT FROM AUTHOR]

Dose prediction tools making use of existing knowledge on the environmental behaviour of radiocontaminants are essential for justification and optimisation of recovery countermeasure strategies for contaminated inhabited areas. In this context, one necessary requirement is to estimate the relative initial contaminant distribution on different types of surfaces in the environment and the resultant initial dose rates to humans staying in the environment. This paper reports on the latest parametric refinements in this context for use in the ERMIN inhabited area dose model, which is an integral part of the European emergency management decision support systems ARGOS and RODOS. • Deposition relations on urban surfaces of a wide range of contaminants are estimated. • Deposition parameters are given for different types of weather conditions (wet/dry). • The deposition data contains uncertainty estimates for use in decision making. • Dose rate conversion factors used in European decision support were recalculated. [ABSTRACT FROM AUTHOR]

• A novel evaluation framework for storm surge disaster emergency plans is established. • The social network theory is introduced to evaluate the trust relationships. • An UPMSM operator is used to aggregate the unreasonable hybrid information. • A DELGDM model for storm surge disaster evaluation is proposed. Storm surges are natural disasters that cause abnormal fluctuations in seawater owing to severe atmospheric disturbances. Storm surges have the characteristics of a fierce, fast and destructive force, and can cause serious casualties and economic losses in coastal areas. To minimize losses within the shortest time and with less material consumption, it is technological to improve emergency decision-making capacity. Therefore, in this paper, a dynamic emergency large-group decision-making (DELGDM) model is proposed to technological evaluate multiple emergency plans. A novel evaluation framework of storm surge disaster emergency plans is firstly established from the two dimensions of the objectives of storm surge emergency response plan and the characteristics of storm surge emergency response action. The uncertain power Maclaurin symmetric mean (UPMSM) operator is then proposed by considering that the unreasonable assessment of large-group decision-makers (DMs) can affect the decision results and the interrelationship among input parameters. Moreover, the social network theory is introduced to evaluate the trust relationships among large-group DMs, and the weight of experts is determined by combining the influence of experts and their knowledge and experience level. Finally, the effectiveness of the proposed method is verified through a case study on storm surge emergency plan optimization. The method is applicable to the optimization of emergency decision-making and provides ideas for solving the selection of various emergency plans. [ABSTRACT FROM AUTHOR]

Abstract: This paper explores the use of utility and game theory to model egress decisions for exit choices found in evacuations. These mathematically rigorous theories serve as a basis for individual exit decision making that captures interactions between evacuees. The model presented in this paper is fundamentally different from traditional evacuation simulators that capture the exit selection behaviour through simple heuristics or objective functions. A utility function based on energy consumption of exit alternatives is created that captures evacuee risk preferences and beliefs. Multiple game forms are created to allow for trade-offs between model fidelity and computational complexity. These models range from Bayesian games to simplified normal games. Multiple examples and validations are used to show that the decision analysis model developed here captures natural human tendencies and characteristics. This enables creation of a high fidelity exit decision model that simulates exit selection of evacuees. [Copyright &y& Elsevier]

Abstract: Foresight the ability to plan and think systematically about future scenarios in order to inform decision-making in the present has been applied extensively by corporations and governments alike in crisis management. Foresight can be complicated because dispersed groups have diverse, non-overlapping pieces of information that affects an organization's ability to detect, mitigate, and recover from failures. This paper explores the failure of foresight in crisis management by drawing on data on events that preceded and followed the Mari disaster in a naval base in Cyprus in July 2011, where a large explosion killed 13 people and injured 62 others, while completely destroying the major power plant of the island. The paper examines how foresight into crisis management decisions was compromised because of a conscious effort by high ranking decision-makers to minimize emergent danger and avoid responsibility for the crisis, in joint with red tape, bureaucracy, and poor coordination and information flows. The paper explores the notion of operational and political responsibility of individual decision-makers and discusses an alternative approach to foresight in crisis management, one that is built on multiple layers of decision-making. [Copyright &y& Elsevier]

Abstract: Urban management requires assessing bioterrorism response in micro-spatial environments, modeling potential attacks and carrying out efficient risk assessment. This paper presents the method of crowd evacuation simulation for bioterrorism in micro-spatial environments using the basic theory of Virtual Geographic Environments (VGE), combined with pathogen diffusion and crowd simulation modeling techniques. A framework of evacuation simulation was presented in this paper and the key technologies to implement the framework, including grid-based spatial data environment, agent-based individual model, and the method of associated individual behavior with spatial environment were also proposed to simulating crowd behaviors in bioterrorism. Taking the Olympic Park station of the Beijing Metro Line 8 as a typical study area, a crowd evacuation simulation prototype system was developed and a quasi-experiment using three different scenarios was conducted. The prototype system can be used to demonstrate some emergent individual behaviors in bioterrorism, such as competitive, queuing, and herding. Details can be obtained at each computation step (e.g., the number of people in subway station, the region of population, the geo-location of individual, the exposed population in polluted areas) with the prototype system. In the experiments, the number of people exposed is constantly increasing with the gradually increase of contaminant area as time pass. And different scenes were appeared for the different way of deal with bioterrorism. With the experiment, the number of people affected and the relevant parameters under various bioterrorism scenarios can be estimated, which can support rapid and efficient emergency decision-making for bioterrorism prevention. [Copyright &y& Elsevier]

Highlights • A new method is proposed to identify critical success factors in emergency management. • The evaluations of influencing factors are given by decision makers using 2-dimention uncertain linguistic variables. • The cause-effect classification of factors are found out based on an improved DEMATEL technique. • An example is presented to illustrate the practicality and usability of the developed method. Abstract Due to the variety and destructiveness of unconventional emergencies, it is of great necessity to analyze and optimize emergency management systematically. The performance of emergency management is often influenced by a lot of factors, and it is not realistic to improve all influential factors simultaneously due to limited resources. To address this concern, a new combined method based on 2-dimension uncertain linguistic variables (2DULVs) and decision-making trial and evaluation laboratory (DEMATEL) is proposed in this paper to determine critical success factors (CSFs) in emergency management. Specifically, the evaluations of influential factors given by decision makers are represented and processed by using 2DULVs. Based on the DEMATEL technique, the cause-effect classification of factors are obtained and the CSFs in emergency management can be found out. Finally, a case study is provided to illustrate the practicality and usefulness of the proposed 2-dimension uncertain linguistic DEMATEL (2DUL-DEMATEL) method. The results show that by enhancing the performance of five CSFs, the effectiveness and efficiency of emergency management could be promoted greatly. [ABSTRACT FROM AUTHOR]

The paper presents a semi-quantitative methodology at a local scale, developed to increase the efficacy of Land Use Planning related to the Management of risks, in particular as far as it concerns multiple risks impinging on the same territory (Multi-risks). At the moment, each risk is managed through a dedicated sectorial plan, having its proper procedures and scale, and the only “meeting point” for these plans – at least in Italy - are the Municipal city plans. The Municipalities have to implement the contents related to the various risks and directly intervene on the territory, but the lack of linkage and coordination between the plans and the authorities in charge often makes the emergency management and LUP less effective towards the achievement of a real safety of territories. In addition, the actual legislative framework does not face the possible consequences of risk interactions. In this context, the objective was to develop a simple risk pre-screening tool, expressly designed for local planners, able to point out the areas more exposed to risks and risks interactions, in order to better address the distribution of the municipal resources for further studies and interventions. The local planners that, especially in Italy, have a central role for the risk management of the territory, became the central point for the proposed framework, assuming the role of evaluators, and then decision-makers. A semi-quantitative approach, based on an index scale from 0 to 3 onwards was developed for a direct use from Municipal technicians; the proposed scale is applied to measure both the impact of the risks and risk interaction. The methodology is composed by 4 steps: 1) characterization of the risks; 2) assignation of the ratings to the risks; 3) assessment of binary risk interactions; 4) assessment of the compatibility and planning phase. Each step is accompanied by GIS mapping. The methodology was tested on two Italian case-studies, two Municipalities affected by multiple types of risks which could interact; the proposed approach demonstrated to be able in identifying and bring multi-risks aspects to the attention of the decision makers, constituting a guide to risk that can be integrated with the existing planning instruments to improve the quality of decisions related to risks. [ABSTRACT FROM AUTHOR]

Abstract: Spatial data and related technologies have proven to be crucial for effective collaborative decision-making in disaster management. However, there are currently substantial problems with availability, access and usage of reliable, up-to-date and accurate data for disaster management. This is a very important aspect to disaster response as timely, up-to-date and accurate spatial data describing the current situation is paramount to successfully responding to an emergency. This includes information about available resources, access to roads and damaged areas, required resources and required disaster response operations that should be available and accessible for use in a short period of time. Any problem or delay in data collection, access, usage and dissemination has negative impacts on the quality of decision-making and hence the quality of disaster response. Therefore, it is necessary to utilize appropriate frameworks and technologies to resolve current spatial data problems for disaster management. This paper aims to address the role of Spatial Data Infrastructure (SDI) as a framework for the development of a web-based system as a tool for facilitating disaster management by resolving current problems with spatial data. It is argued that the design and implementation of an SDI model and consideration of SDI development factors and issues, together with development of a web-based GIS, can assist disaster management agencies to improve the quality of their decision-making and increase efficiency and effectiveness in all levels of disaster management activities. The paper is based on an ongoing research project on the development of an SDI conceptual model and a prototype web-based system which can facilitate sharing, access and usage of spatial data in disaster management, particularly disaster response. [Copyright &y& Elsevier]

Decision-making is the most important business activity and becomes more complex in the current big-data situation. Most organizational decision-making is made in a group and has the data analytics function to seek specific answers for specific purposes. Multi-attribute group decision-making (MAGDM) methods provide effective support to decision groups by evaluating and integrating individual group members opining. However, current MAGDM methods often suffer from the problem of opining data and decision environment uncertainty, which is particularly severe in a large decision group or a newly decision problem. As a solution, rough sets and fuzzy sets have been applied in MAGDM to deal with data and decision process uncertainties. Although a lot of efforts have been made in applying rough sets and fuzzy sets to deal with data and decision process uncertainties the area, the disadvantage of rough set models in classification accuracy when similar class as basic knowledge granularity has not been well solved yet. This paper aims to solve this problem by introducing a new concept-maximal consistent block (MCB) and multi-granulation decision-theoretic rough set (MCB-MDTRS) models. Firstly, it establishes a binary tolerance relation on the universe of discourse, defines generalized MCBs, and introduces pessimistic DTRS based on MCBs. Secondly, it extends objective set to a fuzzy environment and proposes four MCB-MDTRFS models with consideration of the weight of each attribute. The steps of the proposed fuzzy MAGDM methods are carefully described in detail. Different from existing fuzzy MAGDM methods, the weight of each attribute is considered in the determination of the positive ideal decision objective and negative ideal decision objective in this paper, named by qualified and unqualified (fuzzy) sets. We solve the vague problem of individual preference evaluation and the uncertainty of setting ideal decision objectives by using the advantages of fuzzy set and rough set theory. Finally, it takes emergency plan selection as a case study to analyze the effectiveness of our methods and compare with other fuzzy MAGDM methods. Our methods outperform the selection of basic knowledge granularity, the determination of ideal decision objective sets and the ranking method, so as to increase the reliability and accuracy of ranking evaluation index. [ABSTRACT FROM AUTHOR]

In serious emergencies, as in the case of floods and extreme weather conditions, where a substantial number of people are involved and over vast areas which may also involve different provinces, currently civil protection planning carries on emergency management operations within rigid schemes. A procedure that would be capable of handling events acquiring data continuously and developing real time solutions in a highly flexible manner has not yet been proposed. This research focuses on how the systematization of information systems and communication processes can improve the management of emergencies caused by extreme weather and climate events. The objectives of improved service, levels of safety and sustainability of the intervention in emergencies would be obtained through a centralized decision support system. The system and tools that are presented in this paper aim to respond to emergency issues dynamically responding to the dynamics of the events by taking advantage of an information system capable of sharing data, notifications, service orders, appeals for help, information on the status of the transport network and any other information. The system would provide decision support by acquiring information from smartphones and other nomadic devices; it would so provide exchange of information in real time on one or more virtual platforms among stakeholders and between them and the citizens. Substantially, the system is based on smartphone applications coupled with a central management emergency Decision Support System specifically built to make best use of the possibilities offered by the latest telematics systems and cooperative web and phone-based tools. [ABSTRACT FROM AUTHOR]

• Develop a complete problem-solving approach for group decision making. • Orient complex large-scale groups and time-sensitive emergency decision scenarios. • Design a feasible mechanism of group consensus strategies under time pressure. • Propose feedback recommendation strategies with a subgroup identification method. • A real case study of China's fight against the COVID-19 epidemic. Group Decision Making (GDM) has been well studied in the last two decades. Yet, two challenges exist: (a) how to resolve large-scale groups in GDM and achieve the consensus of preferences and (b) how to conduct GDM under risk and emergency conditions. In this paper, we develop a complete problem-solving approach for GDM that orients twofold settings of the complex large-scale group and the time-sensitive emergency decision scenarios. The crux of the matter is to design a feasible mechanism of group consensus strategies in the environment of time pressure and natural language preferences. To solve this problem, we propose a closed-loop mechanism of feedback recommendation strategies accompanied with a new subgroup identification method. This mechanism is underlain by a fourfold decomposition of complex large-scale groups, which entails multiple thresholds of group consensus, group hesitation, and time-related iteration of loops. Our mechanism and the whole GDM approach thoroughly orient the most intuitive representation of preferences - human natural language, which can be elicited and quantitatively formulated in probability linguistic preference systems. We illustrate the proposed approach through a real case study of China's fight against the COVID-19 epidemic. We verify that our mechanism can perfectly tradeoff between the effectiveness and the efficiency of complex large-scale GDM under risk and emergency. The results of this research provide proposals for mechanisms on large-scale GDM and are expected to contribute to emergency management such as epidemic controls, anti-terrorism, and other man-made or natural hazards. [ABSTRACT FROM AUTHOR]

The need for exit signs that attract attention when they need to be conspicuous, to redirect people to not just an exit route, but a viable and if possible an optimal exit route in an evolving emergency has driven the development of a new generation of advanced signage, the Intelligent Active Dynamic Signage System (IADSS). The IADSS, attempts to meet these needs through the development of a novel Active Dynamic Signage System (ADSS) which can be controlled by an intelligent decision support system. This paper presents results that refine the effectiveness of the ADSS. When an exit route is considered non-viable, the adapted ADSS provides not only negative information i.e. that an exit is no longer available, but also positive information, i.e. indicating a viable alternative exit. In addition, in situations where a voice alarm system is used in conjunction with the ADSS, it is also important to ensure that the messaging does not contradict the intent of the ADSS. The improved ADSS was tested in full-scale evacuation trials in a rail station. Results suggest that the improved ADSS with voice alarm messaging that does not contradict the signage system successfully redirected 66% of the participants to the intended exit. [ABSTRACT FROM AUTHOR]

Anticipating the future is a key practice for the management of potential emergencies. Anticipatory action needs the future to become ready-to-hand. Focusing on the logics and practices of anticipatory action the paper discusses the relations between time and space in the context of risk and uncertainty. Spatializations of simulation technologies, preemptive emergency management and anticipatory action aim to disclose and extrapolate the future. In general, infrastructures are technologies which aim to materialize expectations concerning the future. In the case of emergency management infrastructural measures enable and/or constrain practices by inheriting specific logics. The concept of riskscapes (Müller-Mahn and Everts, 2013) poses to be a promising framework to grasp these issues. In our perspective, extrapolated riskscapes treat the future as an already interpreted and symbolically structured world. This involves not only looking at the temporality of riskscapes, but also dealing with geographies of inscribed futurity. Two case studies focusing on emergency management practices of firefighters will be deployed for illustration: the first observes the logics of preemptive emergency management and anticipatory action inscribed into materialities of infrastructures in the context of rail-bound hazmat transports; the second shows how computer simulations for crowded geographies facilitate decision-making and action for policing and crowd management. Instead of treating future in riskscapes as neutral, we highlight the politically situated practices that co-evolve with these technologies and their spatializations. The article discusses the dimension of time within riskscapes to gain a better understanding of the temporalization of space as in simulations and the spatialization of time as in infrastructures of emergency management. [ABSTRACT FROM AUTHOR]

Traffic evacuation is a critical task in disaster management. Planning its evacuation in advance requires taking many factors into consideration such as the destination shelter locations and numbers, the number of vehicles to clear, the traffic congestions as well as traffic road configurations. A traffic evacuation simulation tool can provide the emergency managers with the flexibility of exploring various scenarios for identifying more accurate model to plan their evacuation. This paper presents a traffic evacuation simulation system based on integrated multi-level driving-decision models which generate agents’ behavior in a unified framework. In this framework, each agent undergoes a Strategic, Cognitive, Tactical and Operational (SCTO) decision process, in order to make a driving decision. An agent’s actions are determined by a combination, on each process level, of various existing behavior models widely used in different driving simulation models. A wide spectrum of variability in each agent’s decision and driving behaviors, such as in pre-evacuation activities, in choice of route, and in the following or overtaking the car ahead, are represented in the SCTO decision process models to simulate various scenarios. We present the formal model for the agent and the multi-level decision models. A prototype simulation system that reflects the multi-level driving-decision process modeling is developed and implemented. Our SCTO framework is validated by comparing with MATSim tool, and the experimental results of evacuation simulation models are compared with the existing evacuation plan for densely populated Beijing, China in terms of various performance metrics. Our simulation system shows promising results to support emergency managers in designing and evaluating more realistic traffic evacuation plans with multi-level agent’s decision models that reflect different levels of individual variability of handling stress situations. The flexible combination of existing behavior and decision models can help generating the best evacuation plan to manage each crisis with unique characteristics, rather than resorting to a fixed evacuation plan. [ABSTRACT FROM AUTHOR]

The concept of resilience is used by many in different ways: as a scientific concept, as a guiding principle, as inspirational ‘buzzword’, or as a means to become more sustainable. Next to the academic debate on meaning and notions of resilience, the concept has been widely adopted and interpreted in policy contexts, particularly related to climate change and extreme weather events. In addition to having a positive connotation, resilience may cover aspects that are missed in common disaster risk management approaches. Although the precise definition of resilience may remain subject of discussion, the views on what is important to consider in the management of extreme weather events do not differ significantly. Therefore, this paper identifies the key implications of resilience thinking for the management of extreme weather events and translates these into five practical principles for policy making. [ABSTRACT FROM AUTHOR]

Due to the variety and destructiveness of disasters, emergency management has emerged as a world theme and attracted more and more public attention. A large quantity of research on optimizing the emergency management has been done while still has improvement space. In this paper, a new method called D-DEMATEL which combines D number theory and decision-making trial and evaluation laboratory (DEMATEL) to identify the critical success factors (CSFs) in emergency management is proposed. Firstly, multiple experts evaluate the direct relations of influential factors in emergency management respectively from positive and negative side. The evaluation results are presented as intuitionistic fuzzy numbers (IFNs). Secondly, convert the IFNs into D numbers and use the combination rule of D numbers to fuse group opinions. Thirdly, based on DEMATEL, the cause-effect classification of factors can be obtained. Finally, the factors in cause category are identified as CSFs in emergency management. Based on the proposed method, the optimization of emergency management can be efficiently simplified into optimizing the identified CSFs. The proposed method is well addressed the fuzziness and subjectivity in linguistic assessment. Hence, the proposed method is well applicable to identify the CSFs in emergency management which inherently has a linguistic assessment process. [ABSTRACT FROM AUTHOR]

Communities have been described to be at the heart of the preparedness for and the response to disasters. The increasing connectedness has made communities more vulnerable for their dependence on a complex network of critical infrastructures. At the same time, this very connectedness has the potential to enable communities to self-organise, engage, and connect with other communities to improve their resilience. While the pathway to more resilience is promising and has many advocates, the response to crises and disasters, time and again reveals the challenges related to (i) ad-hoc switching from preparedness to response; (ii) ad-hoc connecting professional responders, communities, volunteers, and local authorities; and (iii) designing systems and tools that are tailored such that the feedback from local communities can be taken into account for coordination and planning. Therefore, a paradigm shift is needed in designing crisis and disaster management information systems linking ad-hoc response to longer-term planning, in which networks of communities are at the core of the process. This paper sets out to provide a critical review on community-resilience literature. From there, it develops a research design principles for information systems to improve community resilience. [ABSTRACT FROM AUTHOR]

This paper reports on the application of a remotely designed appropriate technology (AT) to emergency needs in a natural disaster. Significantly, the present strategy was compared and contrasted with that of a traditional sustainable community development (SCD) strategy. In response to a large earthquake in Nepal, engineering students at Metropolitan State University of Denver (MSU Denver) examined technological interventions for the emergency needs of affected communities in the disaster's immediate aftermath. Using community information acquired through interviews and secondary data, they developed a simple biosand filter that is inexpensive, easy to build and made from materials that are available, accessible and affordable to beneficiaries. Students chose a design, built it, tested it and wrote instruction manuals in both Nepali and pictogram formats. These manuals were then distributed through NGO partners doing relief work in Nepal. Finally, this study explores how the use of an AT assessment tool–originally designed for decision support in the project design phase–might instead be used for monitoring and evaluation of AT effectiveness in an emergency response scenario. [ABSTRACT FROM AUTHOR]

An analysis of the effects of potential oil spills will provide data in support of decisions related to improving the response to oil spills and its emergency management. We selected the Chinese Bohai Sea, especially the Bohai Strait, as our investigation region to provide an assessment of the effects of pollution from ship-related oil spills on adjacent coastal zones. Ship-related accidents are one of the major factors causing potential oil spills in this area. A three dimensional oil transport and transformation model was developed using the Estuary, Coastal, and Ocean Model. This proposed model was run 90 times and each run lasted for 15 days to simulate the spread and weathering processes of oil for each of four potential spill sites, which represented potential sites of ship collisions along heavy traffic lanes in the Bohai Sea. Ten neighboring coastal areas were also considered as target zones that potentially could receive pollutants once oil spilled in the study areas. The statistical simulations showed that spills in winter were much worse than those in summer; they resulted in very negative effects on several specific target zones coded Z7, Z8, Z9, and Z10 in this paper. In addition, sites S3 (near the Penglai city) and S4 (near the Yantai city) were the two most at-risk sites with a significantly high probability of pollution if spills occurred nearby during winter. The results thus provided practical guidelines for local oil spill prevention, as well as an emergency preparedness and response program. [ABSTRACT FROM AUTHOR]

Potential exposures are those that may occur as a result of unanticipated operational performance or accidents. Potential exposure situations are probabilistic in nature because they depend on uncertain events such as equipment failure, operator errors or external initiators beyond the control of the operator. Consequently, there may exist a range of possible radiological impacts that need to be considered. In this paper a Level 3 Probabilistic Safety Assessment (PSA) for a hypothetical scenario relevant to Innovative Nuclear Energy Systems (INS) was conducted using computer code MACCS (MELCOR Accident Consequence Code Systems). The acceptability of an INS was analyzed taking into account the general requirement that relocation or evacuation measures must not be necessary beyond the site boundary. In addition, deterministic modeling of the accident consequences for the critical meteorological conditions was carried out using the JRODOS decision support system (Real-time On-line Decision Support system for off-site emergency management in Europe). The approach used for dose and risk assessment from potential exposure of accidental releases and their comparison with acceptance criteria are presented. The methodology described can be used as input to the licensing procedure and engineering design considerations to help satisfy relevant health and environmental impact criteria for fission or fusion nuclear installations. [ABSTRACT FROM AUTHOR]

• Feature engineering based framework for location-sensitive disaster event detection. • Augmentation of context-free and context-based features with place of occurrence. • Evaluation with unsupervised machine learning algorithm with 09 performance metrics. • AHP-TOPSIS based selection of an efficient machine learning algorithm and feature set. • Location-augmented context-based features outperformed traditional textual features. Disaster event detection aims to identify events like terrorist attacks, fire incidents, stampede incidents, building collapse, etc., reported in the online news articles or social media. Place of occurrence of disaster event is a significant feature associated with events for location-sensitive disaster event detection. Efficient feature selection and their augmentation with location information can contribute towards the evolution of traditional approaches and their adoption for location-sensitive disaster event detection leading to improvement in the overall process as a whole. Since the evaluation of event detection techniques deliberates various intrinsic and extrinsic performance metrics, the decision-making for the selection of feature sets is treated as a Multiple-Criteria Decision Making (MCDM) problem. This paper proposes a framework, GeoClust , that is based on feature engineering of traditional textual features in order to enhance their capability for improved location-sensitive disaster event detection. The framework augments context-free and context-based textual feature sets with feature sets of place of occurrence of the events and evaluates their performance using unsupervised machine learning algorithms for various performance metrics. Finally, the best feature set is selected using AHP-TOPSIS technique of MCDM in order to tune the system for automatic and efficient location-sensitive disaster event detection in real-time. Extensive set of experiments have been performed in order to evaluate the framework on a dataset of online news articles reporting disaster events about terrorist attacks, fire incidents, stampede incidents, building collapse and maoist attacks happened at different locations in India. The results show that the location-augmented feature sets significantly improve performance of location-sensitive disaster event detection as compared with traditional feature sets. The results also demonstrate that the context-based feature sets with location-augmentation are ranked higher than the context-free feature sets in MCDM analysis. [ABSTRACT FROM AUTHOR]

Earthquakes can have a tremendous impact on the operations of university campuses. Since campus buildings are used for teaching, living, and entertaining, it is necessary to establish timely and effective emergency response strategies that accord with the functions of each building. Since there is currently no emergency response system that can provide real-time feedback and enable decision making during an earthquake, this paper proposes a university emergency response system based on multi-source data monitoring. The proposed system offers priorities for rescuing people and maintaining facilities according to five indices: the number of people in the building, the building's command role, the potential danger of hazardous chemicals in the building, the extent of damage to the building, and the lifeline role of facilities in the building. Several methods are used in the process of decision making, including the analytic hierarchy process (AHP), linear regression, earthquake disaster simulation, and expert evaluation. The emergency response system is then applied to a case study of the Shahe Campus of the Central University of Finance and Economics in Beijing, where simulations of three typical scenarios under fixed seismic parameters are carried out and compared. The system proposed in this paper makes the campus's earthquake emergency response more rapid and reasonable, providing new ideas for promoting the development of emergency management systems and sustainable campus operations at colleges and universities. Multi-source data based university emergency response system is proposed. The decision framework is timely and effective under limited cost. Specific buildings' electricity consumption reflects the real-time indoor population. Time characteristics and key areas based emergency resources should be allocated. [ABSTRACT FROM AUTHOR]

To solve the problems of heterogeneity and disorder in the cross-domain integration processes for offsite nuclear emergency response, the integrated decision-making framework of the CDIRS spaces based on Space Mapping and Semantic Web is proposed. Under this framework, the method of feature mapping and space modeling, interoperable mediating and interoperability verifying, ordered integrating and reasoning, which are key steps of the CDIRS method, are presented. Furthermore, with a fictive nuclear accident scenario, the relevant CDIRS spaces and ordering rules are constructed, and then the appropriate cross-domain integrated response solution is obtained and verified effectively by reasoning and simulating in the CDIRS model. With the CDIRS method proposed in this paper, the heterogeneity and disorder can be avoided, and the targets and constraints of the offsite nuclear emergency response problems in the original space can be transformed into the target space, being processed unified by formal modeling and ordered reasoning. [ABSTRACT FROM AUTHOR]

Unconventional emergency decision making not only involves intangible and conflicting criteria, but also needs a fast response to the emergency incident under the cases of time pressure and incomplete information. It might be an effective way to make full use of the outlier data of incident information and skip some direct comparisons between alternatives to make a fast emergency decision. Focusing on the missing judgments estimation issue in an incomplete comparison emergency decision matrix, this paper extends the geometric mean induced bias matrix to estimate the missing judgments and improve the consistency ratios at the same time. The least absolute error method and the least square method are used to optimize the revised geometric mean induced bias matrix and find the missing values. A numerical example with incomplete information is used to demonstrate the proposed models. A case of emergency decision making simulation is also conducted to show how the proposed model is applied in practice. The results show the proposed models are not only capable of completing missing values, but also can efficiently improve the matrix consistency at the same time. In addition, the proposed model can aid emergency managers to make a fast response to unconventional emergency in the case of lacking complete information. [ABSTRACT FROM AUTHOR]

Foresight processes help decision makers plan for potential, desirable or probable futures. With increasing unpredictability, under pressure of time and in multi-agency situations, however, traditional foresight and strategic management fail. In this paper, we redefine a foresight process for real time network management instructed by the extreme case of collaboration in global disaster management. We use an actor–network approach to explore on global and local levels emerging networks. We find that ad hoc and long term network dynamics are governance structures for unpredictable collaboration just as traditional goal setting and targeting is conducive to stable environments. Altogether five dynamic network patterns are found that underlie successful ad hoc collaboration: (1) identification of heterogeneous network actors and early alignment of interests (2) development of a shared vision for heterogeneous goals (3) use of boundary objects (4) punctual directness and distance among implementing actors (5) intense local integration of the focal actor. Governmental decision makers, corporate actors and voluntary associations who are cognisant of dynamic network patterns can use them for rapid collaboration instead of long-range foresight and constituent planning. To base leadership on dynamic network principles instead of traditional strategic management means to adopt a new real-time foresight for collaborative innovation. [ABSTRACT FROM AUTHOR]

Disasters are characterized by severe disruptions in society׳s functionality and adverse impacts on humans, environment and economy. Decision-making in times of crisis is complex and usually accompanied by acute time pressure. Environment can change rapidly and decisions may have to be made based on uncertain information. IT-based decision support can systematically help to identify response and recovery measures, especially when time for decision-making is sparse, when numerous options exist, or when events are not completely anticipated. This paper proposes a case- and scenario-based approach to supporting the management of nuclear events in the early and later phases. Important information needed for decision-making as well as approaches to reusing experience from previous events are discussed. This work is embedded in a decision support method to be applied to nuclear emergencies. Suitable management options based on similar historical events and scenarios could possibly be identified to support disaster management. [ABSTRACT FROM AUTHOR]

Over the past two decades, the impact of disasters has been devastating, affecting 4.4 billion people, resulted in 1.3 million causalities and $2 trillion in economic losses. Post-disaster reconstruction and rehabilitation is a complex process with several dimensions. Government, nongovernmental, and international organizations have their own stakes in disaster recovery programs, and links must be established among them as well as with the community. Concerning post-disaster reconstruction scenario, the most significant factor is prompt decision making based on best possible information available. Effective sustainable post-disaster response is crucial and lies at the heart of disaster management agencies in almost every cautious country around the globe. Development is a dynamic process and disasters provide the opportunities to vitalize and/or revitalize this process, especially to generate local economies, and to upgrade livelihood and living condition. The success of the reconstruction phases, i.e., rescue, relief, and rehabilitation, is mainly dependent on the availability of efficient project teams and timely information to make informed decision. By having the knowledge-based system to make well-informed decisions, combined with the efficiency of a project team and strong coordination, project success should increase. This paper presents a theoretical framework of a knowledge-based approach for enhancing prompt and effective sustainable disaster management. The conceptual model consists of two main IT based components of knowledge- based system, i.e., a knowledge-base and a decision support shell for making more informed decisions for effective, timely and sustainable response in post-disaster reconstruction scenarios. The system is expected to assist in improving reconstruction project processes, coordination, and team building process because the most likely areas on which to focus can be identified during the early stage of the post-disaster scenario. Tapping into the past experiences of post-disaster scenarios, the knowledge-based system provides a wealth of pertinent and useful information for decision makers and will eventually enhance collaborative ventures for sustainable disaster management. The system would be helpful for emergency response management teams to take proactive measures by learning from past similar experiences, making informed decisions related to team building and project coordination processes undertaken by disaster management agencies. Professionals need to work in close cooperation with each other to give rise to a better and more efficient system for sustainable disaster management. Hence, the study is valuable for all professionals involved with research and development of sustainable disaster management strategies. [ABSTRACT FROM AUTHOR]

The behaviour of building occupants before the purposive movement towards an exit, known as the pre-evacuation behaviour, can have a strong impact on the total time required to leave a building in case of fire emergency as well as on the number of casualties and deaths. The pre-evacuation time can be simulated within computational models using different approaches. This work introduces a new model for the simulation of pre-evacuation behaviour based on the Random Utility Theory. The proposed model represents the pre-evacuation behaviour of simulated occupants considering three behavioural states: normal, investigating and evacuating. The model simulates the probability of choosing to start investigating and evacuating in relation to physical and social environmental factors as well as personal occupant characteristics. These two decisions make occupants pass from their starting normal states to investigating and evacuating states. The paper presents a case study of the proposed pre-evacuation time model using an experimental evacuation data set in a cinema theatre. The application of the model allows identifying the main factors affecting the decision to move from a state to another. In the present case study, the main factors influencing the decisions were the time elapsed since the start of the alarm, the occupant’s position, and social influence. The issues associated with the implementation of the model are discussed. [ABSTRACT FROM AUTHOR]

Scenarios are designed to support decision-makers in gaining a better understanding of the consequences of decisions. Despite their popularity in IT for Foresight, a major obstacle is the complexity of strategic decisions and the resulting multitude of scenarios. Therefore choices need to be made, which scenarios to fully work out, by which experts and in which level of detail. This paper presents an approach to support making these trade-offs between accuracy and resources spent by prioritising scenarios based on their significance for the decision even on the basis of incomplete information. This approach combines theoretical findings in decision theory with results that were elicited in interviews and workshops with decision-makers and experts. In this manner, we ensure to adhere to time and effort constraints of the involved experts, while taking into account the decision-makers' preferences and requirements. The emergency management use-case shows that the resulting reduced set of scenarios leads to the same ranking of decision alternatives as the entire set of scenarios. [ABSTRACT FROM AUTHOR]

Debris generated by disasters can hinder relief efforts and result in devastating economic, environmental and health problems. In this paper, we present a decision-support tool to assist disaster and waste management officials with the collection, transportation, reduction, recycling, and disposal of debris. The tool enables optimizing and balancing the financial and environmental costs, duration of the removal operations, landfill usage, and the amount of recycled materials generated. It can support post-disaster operational decisions as well as the challenging task of developing strategic plans for disaster preparedness. [ABSTRACT FROM AUTHOR]

The practice of Revenue Management has received widespread acceptance in the international hospitality industry yet a lack of best practice in terms of organizational integration persists. This paper follows the notion that revenue management is first and foremost a human activity, dependent on knowledge exchange and concerted decision within revenue management teams. One critical attribute of effective teams is group cohesion. The authors contrasted communication networks of 38 revenue management teams by means of social network analysis to identify the antecedents and consequences of group cohesion. It was found that industry employment, age and revenue management experience define the structure of communication networks and that awareness of other's expertise is central in explaining differences team performance across the sample. The findings highlight the issue of knowledge asymmetry in teams and suggest that the Revenue Manager occupies a more active role as an information broker in order to enhance group decision making. [ABSTRACT FROM AUTHOR]

Abstract: Mountain rescue is a new topic emerged after the expansion of emergency rescue function of public security fire force. This paper introduces the classification of mountain disaster incidents, analyzes mountain incidents rescue features. For Police Fire fighting Army which is the professional force and key contingent of emergency rescue, the measures of emergency rescue and the equipments used in mountain disaster incidents are presented, with the aim of offering reference for the implement of the most effective and best rescue measures in similar incidents in future. [Copyright &y& Elsevier]

Abstract: Compared with conventional emergency, unconventional emergency has typical unconventional characteristics and its system characteristics and emergency decision-making processes and methods are also very special. In order to making a deeper understanding the system characteristics and emergency decision-making mechanism of unconventional emergency, based on the definition and features analysis, the principle of entropy and dissipative structure theory to explain unconventional emergency system status characteristics, this paper analysis of unconventional emergencies and emergency management in the process of entropy state changes; further analysis temporal frame of unconventional emergency decision-making. Analysis shows that the unconventional emergencies happened and management is an Open dissipative system. System entropy changing by increased entropy factors ZS, negative entropy factor FS and system bearing capacity C combined action. Corresponding unconventional emergency system in the process of incubation period, initiation period, outbreak period, evolution period or recovery period and disappeared period, emergency decision-making work of temporal framework can be divided into warning precaution, crisis identification and isolation, integrated response and disposal measures four links. [Copyright &y& Elsevier]

Abstract: As a kind of emergency evacuation, the large-scale evacuation is an effective measure to mitigate disaster in a sudden crises. Research into the emergency evacuation of crowds has been the focus of many scholars at home and abroad for some time. In this paper, four principal aspects of current research into large-scale crowd evacuation, namely evacuation theories, evacuation modeling, evacuation decision-making and evacuation risk evaluation have been summarized. Of these, evacuation modeling has attracted the greatest interest. While some evacuation models have proved effective tools in evacuation decision-making and risk evaluation, existing evacuation models have not fully considered the uncertain factors in the process of large-scale evacuation. [Copyright &y& Elsevier]

Abstract: With the increasing number of subway stations, more and more attention has been paid to their emergency evacuation, as it plays an important part in urban emergency management. The present paper puts forward a method of crowd evacuation simulation for bioterrorism in subway station environment using the basic theory of the Social Force Model combined with the Gaussian Puff Model. A Combined Social Force Model is developed which is suitable for a real situation where there is a sudden toxic gas event. The model can also be used to demonstrate some individual behaviors in evacuation, such as competitive, grouping and herding. At last a series of experiments are conducted and the results are as follows. (1) When there is a toxic gas terroristic attack in subway stations, the influence on passengers varies according to the position that the gas source lies in and the numbers of gas sources. (2) More casualties will occur if managers do not detect the toxic gas danger and inform passengers about it. (3) The larger the wind speed is, the smaller the number of injured passengers will be. With the experiments, the number of people affected and other parameters like gas concentration can be estimated, which could support rapid and efficient emergency decisions. [Copyright &y& Elsevier]

Abstract: In this paper we analyze how to apply cross-impact modeling for developing collaborative scenarios in Emergency Preparedness. Scenarios can enhance the understanding of emergency teams about the factors which are involved in the definition of an emergency plan and how different actors participate in it. The version of Cross-Impact Analysis described aims at contributing to this goal through allowing the collaborative development of scenarios out of large event sets. This ultimately reduces the complexity for estimating a working model. In order to illustrate this research effort hypothetical results of a dirty bomb attack scenario exercise are presented, along with the final estimates of relationships based on four rounds of individual estimates followed by discussion of differences in the perceived relationships, in order to achieve a “consensus” model. The purpose of this exercise is to demonstrate the ability of a group to create a working model of the scenario that may be used to examine the consequences of various assumptions about preparedness, plans, and the actions taken during the crisis situation. The method may be used as either a planning tool and/or a training tool. We discuss the process for collecting inputs from a collaborative group and how to improve the consistency of the group inputs in a Delphi-like feedback process. Suggestions for improving details of the wording of items in order to minimize misunderstandings and miscommunication are included, along with suggestions for future extensions to this research. [Copyright &y& Elsevier]

Abstract: Community resilience is a term that describes the community's ability to function amidst crises or disruptions. Community resilience is perceived as a fundamental element in emergency preparedness and as a mean of ensuring social stability in the face of crises, including disasters. However, there is a paucity of empiric evidence for this conjecture. This paper demonstrates the use of the Conjoint Community Resilience Assessment Measurement (CCRAM) for estimating the ability of a community to be resilient in the face of disaster. Six factors of community resilience were identified based on a study conducted in nine small to medium size towns (N=886): Leadership, collective efficacy, preparedness, place attachment, social trust and social relationship. Multiple logistic regressions yielded the CCRAM protective factors for perceived community resilience. Receiver Operating Characteristic (ROC) analysis confirmed the quality of the CCRAM as a diagnostic tool for perceived community resilience. The CCRAM tool is presented as a potential provider of information for authorities and decision makers as an aid for foreseeing and planning towards the challenges present during emergency times. [Copyright &y& Elsevier]