Decision support system network analysis for emergency applications

Emergency Assessment and Decision Support System has become an important tool to effectively respond to a nuclear emergency in nuclear power plant, the system mainly used to on-line assessment the real-time reactor conditions, calculate the source term released to the environment, and suggest the protection actions taken to protect the public and emergency workers. This paper describe the development of the system, analysis the business flow chart of nuclear assessment, introduce details of several functional modules, such as core damage assessment, source term calculation and radiological consequence assessment. The system’s data structure and data interface also be describe in detail.

Hazardous materials road transportation accidents would cause great harm to neighboring population and environment. It is essential to develop a decision support system (DSS) for emergency response of hazardous materials road transportation accidents to improve emergency response ability and reduce accidents hazards. A framework based on quality function deployment (QFD) method to design DSS scheme selection was proposed. First of all, this paper identified DSS design scheme selection problem, which took into the requirements of government departments and transport enterprises, then applied the method based on QFD to lead the user's requirements into technical attributes of DSS design. In addition, Fuzzy set theory was applied in the QFD method, to evaluate the linguistic terms and variables in questionnaire and evaluation. Finally, a real case was given to illustrate the feasibility of the proposed method.

Artificial intelligence (AI)-based emergency response systems have emerged as critical enablers of smart infrastructure safety, offering enhanced real-time decision-making, risk assessment, and disaster mitigation strategies across various domains. This systematic literature review, encompassing 424 eligible studies, investigates the integration of machine learning (ML), deep learning (DL), computer vision, IoT-enabled predictive analytics, and AI-powered robotics in optimizing emergency response mechanisms. The study comprehensively examines AI applications in disaster management, real-time incident detection, healthcare emergency response, industrial hazard prevention, cybersecurity frameworks, and intelligent traffic control, providing a detailed assessment of technological advancements and challenges in AI adoption. The findings reveal that AI has significantly improved predictive accuracy, automated hazard detection, and emergency resource optimization, leading to faster response times, minimized human error, and enhanced situational awareness in crisis management. AI-driven predictive analytics models have enabled early warning systems for earthquakes, floods, and wildfires, facilitating proactive disaster preparedness and risk mitigation. In real-time emergency response, AI-powered computer vision and sensor-based surveillance technologies have improved incident detection, reducing intervention delays and ensuring more efficient allocation of emergency resources. In the healthcare sector, AI-enhanced diagnostic tools, triage automation, and geospatial analytics for ambulance dispatch have streamlined medical crisis management, improving survival rates and reducing treatment delays. Additionally, AI-integrated industrial safety frameworks, robotic automation, and cybersecurity intelligence systems have strengthened workplace hazard prevention, cyber threat detection, and emergency communication resilience, ensuring safer and more secure operational environments. Despite these advancements, several challenges related to interoperability, regulatory constraints, cybersecurity vulnerabilities, algorithmic biases, and ethical concerns persist, hindering large-scale AI adoption in emergency response systems. This review provides a comprehensive synthesis of AI’s transformative role in modern emergency management, offering insights into technological developments, limitations, and policy considerations necessary to enhance AI-driven crisis response strategies and ensure more effective, scalable, and resilient emergency safety infrastructures worldwide.

Emergency decision support system (EDSS) is a very useful method to improve the level of emergency management. How to build an open, comprehensive and flexible emergency decision support system to handle various public emergencies and integrate various models and methods, has become a focus in the theoretical study and practical applications. Firstly, the general decision process of emergency management is proposed. Then, as the starting point and the end result of decision-making, the problem is studied in detail, and problem model is presented. Finally, the architecture of EDSS base on general decision process is proposed.

The emergency decision support system proposed in this paper uses data mining technology to analyze and mine a large amount of dynamic operation data and historical operation data in the China-ASEAN natural disaster database, which can be used to analyze the dynamic safety risk and optimize the emergency rescue and disposal plan for China-ASEAN natural disasters. In order to optimize the emergency decision support system, the core algorithm of data mining in the system is improved, an improved sequence analysis algorithm is proposed, and improvement measures are proposed on the basis of the inadequacy of the original Apriori algorithm. Through examples, the data mining algorithm’s specific process of automatically generating response suggestions for security events is demonstrated. The analysis of the data shows that the test results meet the expected results, and the algorithm can effectively make response decisions. The average false detection rate is only 8.05%. In scenarios 2, 3, and 4, all of them have higher emergency response efficiency, respectively, of 0.4445, 0.4616, and 0.5299. The completion time of the emergency response increases as the response level increases.

A new emergency decision support system: the automatic interpretation and contextualisation of events to model a crisis situation in real-time

In the analysis of the features of emergency decision and emergency decision support system it can find that the effective application of emergency decision and support systems must rely on the specific models and methods to achieve high efficiency and scientific decision in a short time because of the specific decision requirements and decision environment. Events retrieval is based on similarity theory in general, and it finds similar events by calculating the similarity, so as to provide reference for decision-making. Based on in-depth research of the conception and characteristics of the emergencies it proposed a multidimensional data model in this paper that divided the event attributes into different type, structured and unstructured. The model calculated attributes similarity according to different types, further analysis of the events similarity.

The key of building smart platform in information security is the emergency response and handle events of the realization mechanism of the system intelligence response and the control center. It is equal to the role of the human brain, the basis of knowledge base of problem-solving strategy based on system security incident, case base, and network security expert knowledge base. It consists of an emergency report subsystem and emergency response system, providing emergency response and processing services to assist the emergency response database and emergency response of the expert system. Information security emergency response and processing system focus on information security of the emergency response technology, they are: intrusion detection technology, accident diagnostic technology, against source separation technology and rapid recovery technology, network attack technology, and computer forensics tracking technology.KeywordsInformation securityInformation security emergency responseComputer forensics

GIScience Research at the Thirty‐second Annual Esri International User Conference

Accurate and rapid earthquake loss assessments and tsunami early warnings are critical in modern society to allow for appropriate and timely emergency response decisions. In the LEXIS project, we seek to enhance the workflow of rapid loss assessments and emergency decision support systems by leveraging an orchestrated heterogeneous environment combining high-performance computing resources and Cloud infrastructure. The workflow consists of three main applications: firstly, after an earthquake occurs, its shaking distribution (ShakeMap) is computed based on the OpenQuake code. Secondly, if a tsunami may have been triggered by the earthquake, tsunami simulations (first a fast and coarse and later a high-resolution and computationally intensive analysis) are performed based on the TsunAWI simulation code that allows for an early warning in potentially affected areas. Finally, based on the previous results, a loss assessment based on a dynamic exposure model using open data such as OpenStreetMap is performed. To consolidate the workflow and ensure respect of the time constraints, we are developing an extension of a time-constrained dataflow model of computation, layered above and below the workflow management tools of both the high-performance computing resources and the Cloud infrastructure. This model of computation is also used to express tasks in the workflow at the right granularity to benefit from the data management optimisation facilities of the LEXIS project. This paper describes the workflow, the associated computations and the model of computation within the LEXIS platform.

In all fields of human society, occasional emergencies are almost inevitable. Once an emergency occurs, rapid and proper decision making is required. The purpose of this paper is to explore the design and development of computerized support systems for emergency decision making (EDM). First the characteristics of EDM problems are examined. Then, in view of limited human computer rationality, requirements for a computerized support system for EDM are determined. A conceptual structure for knowledge-based distributed emergency decision support systems is proposed. Finally, a prototype system for safety protection and disaster response in coal mines, developed using the proposed structure, is briefly described.

This article examines the underlying architecture guiding the development and use of enterprise decision support systems that maintain the delivery of time critical public services. A normative architecture, developed from comparative cases involving San Mateo County and Mayo Clinic Emergency Medical Services systems, provides a collection of characteristics meant to guide an emergency response system toward a high level of performance and enable optimal decision-making. At a national symposium, academics and practitioners involved in promoting effective emergency response information systems provided validation for the architecture and next steps for enhancing emergency response information systems. Normative architecture characteristics and expert perspectives from the symposium are integrated into a framework that offers an enterprise approach for delivering time-critical emergency response services. This article provides recommendations for navigating toward a more incremental approach in developing enterprise-oriented emergency information services and examines future trends involving the application of normative architectural concepts to real-world emergency medical settings.

This article examines the underlying architecture guiding the development and use of enterprise decision support systems that maintain the delivery of time critical public services. A normative architecture, developed from comparative cases involving San Mateo County and Mayo Clinic Emergency Medical Services systems, provides a collection of characteristics meant to guide an emergency response system toward a high level of performance and enable optimal decision-making. At a national symposium, academics and practitioners involved in promoting effective emergency response information systems provided validation for the architecture and next steps for enhancing emergency response information systems. Normative architecture characteristics and expert perspectives from the symposium are integrated into a framework that offers an enterprise approach for delivering time-critical emergency response services. This article provides recommendations for navigating toward a more incremental approach in developing enterprise-oriented emergency information services and examines future trends involving the application of normative architectural concepts to real-world emergency medical settings.

Radiological consequence assessments using time-varying source terms in ONERS- decision support system for nuclear emergency response

To study the occupational safety and health systems used for emergency response workers in the USA, we performed interviews with related federal agencies and conducted research on related studies. We visited the Federal Emergency Management Agency (FEMA) and National Institute for Occupational Safety and Health (NIOSH) in the USA and performed interviews with their managers on the agencies' roles in the national emergency response system. We also obtained information prepared for our visit from the USA's Occupational Safety and Health Administration (OSHA). In addition, we conducted research on related studies and information on the website of the agencies. We found that the USA had an established emergency response system based on their National Incident Management System (NIMS). This enabled several organizations to respond to emergencies cooperatively using a National Response Framework (NRF) that clarifies the roles and cooperative functions of each federal agency. The core system in NIMS was the Incident Command System (ICS), within which a Safety Officer was positioned as one of the command staff supporting the commander. All ICS staff were required to complete a training program specific to their position; in addition, the Safety Officer was required to have experience. The All-Hazards model was commonly used in the emergency response system. We found that FEMA coordinated support functions, and OSHA and NIOSH, which had specific functions to protect workers, worked cooperatively under NRF. These agencies employed certified industrial hygienists that play a professional role in safety and health. NIOSH recently executed support activities during disasters and other emergencies. The USA's emergency response system is characterized by functions that protect the lives and health of emergency response workers. Trained and experienced human resources support system effectiveness. The findings provided valuable information that could be used to improve the occupational safety and health function in the Japanese system.