Decision Support System For Response Research Articles

Ontology-based inference decision support system for emergency response in tunnel vehicle accidents

Emergency response plans for tunnel vehicle accidents are crucial to ensure human safety, protect critical infrastructure, and maintain the smooth operation of transportation networks. However, many decision-support systems for emergency responses still rely significantly on predefined response strategies, which may not be sufficiently flexible to manage unexpected or complex incidents. Moreover, existing systems may lack the ability to effectively respond effectively to the impact different emergency scenarios and responses. In this study, semantic web technologies were used to construct a digital decision-support system for emergency responses to tunnel vehicle accidents. A basic digital framework was developed by analysing the knowledge system of the tunnel emergency response, examining its critical elements and intrinsic relationships, and mapping it to the ontology. In addition, the strategies of previous pre-plans are summarised and transformed into semantic rules. Finally, different accident scenarios were modelled to validate the effectiveness of the developed emergency response system.

Rapid Crisis Response in Resilient Cities: Emergency Response to and Reconnaissance of Earthquake Disasters in Taiwan

The devastating Chichi earthquake in Taiwan in 1999 highlighted the importance of disaster prevention and relief operations. In response to the challenge of extreme events and compound disasters, the National Science and Technology Center for Disaster Reduction (NCDR) contributes to operations at the Central Emergency Operation Center (CEOC) of Taiwan by providing integrated information and timely suggestions, and it delivers a common operational picture to central and local governments through a decision support system. This study was undertaken to explore the emergency response towards the reconnaissance of earthquake disasters in Taiwan to illustrate how resilient cities ensure rapid crisis response with a focus on the NCDR disaster response decision support system. This study examines the decision support system used following the 2016 ML 6.6 Meinong earthquake. The results demonstrate that the first line of relief for the emergency response commander is data-driven decision-making.

Design of a University Pandemic Response Decision Support System

The global effort to combat the COVID-19 pandemic has changed how people conduct their daily lives. Institutions of higher education have been greatly impacted by these changes and must find ways to adapt to this new environment. Universities are a unique case because they must control disease spread, while maintaining the same or similar quality of education. The University Pandemic Response Decision Support System (UPRDSS) is a system designed to help universities pick the most suitable method for instruction delivery when faced with any pandemic. Using George Mason University as a case study, the goal was to design a system that allows university administrations to make an educated operations decision. The UPRDSS achieves this by simulating the spread of disease, analyzing learning outcome data, and using a multi-attribute utility function to determine the most appropriate method of instruction that enables positive learning and health outcomes.

Analysis on the Dead Zones of Disaster Management Following Urban Characteristics

To reduce the property and life damages caused by the hazards, it is critical to develop an integrated and complete Disaster Management Information System (DMIS) to identify potential dead zones of disaster management based on urban characteristics. An ideal DMIS should integrate GIS tools with internet technology, dynamic hazard modeling and graphical user interface, and operate through the four phases of disaster management including mitigation, preparedness, response, and recovery. Disaster prevention and hazard response system have been actively studied in many developed countries such as Taiwan, but there is still a need of an integrated information system to better and faster implement the results of the system to the real practices. I n this paper, we f irst described the system framework in Taiwan using GIS and internet technology and then suggested a decision support system for emergency response within the framework identifying dead zones which could be implemented in South Korea. This system is designed to integrate the real-time monitoring data with dynamic hazard models and GUIs to provide disaster management decision support tools for emergency response, which could be useful for policy making in the future.

Next-Generation Smart Response Web (NG-SRW): An Operational Spatial Decision Support System for Maritime Oil Spill Emergency Response in the Gulf of Finland (Baltic Sea)

The Baltic Sea is a unique and sensitive brackish-water ecosystem vulnerable to damage from shipping activities. Despite high levels of maritime safety in the area, there is a continued risk of oil spills and associated harmful environmental impacts. Achieving common situational awareness between oil spill response decision makers and other actors, such as merchant vessel and Vessel Traffic Service center operators, is an important step to minimizing detrimental effects. This paper presents the Next-Generation Smart Response Web (NG-SRW), a web-based application to aid decision making concerning oil spill response. This tool aims to provide, dynamically and interactively, relevant information on oil spills. By integrating the analysis and visualization of dynamic spill features with the sensitivity of environmental elements and value of human uses, the benefits of potential response actions can be compared, helping to develop an appropriate response strategy. The oil spill process simulation enables the response authorities to judge better the complexity and dynamic behavior of the systems and processes behind the potential environmental impact assessment and thereby better control the oil combat action.

A machine learning approach for building an adaptive, real-time decision support system for emergency response to road traffic injuries

In this paper, historical data about road traffic accidents are utilized to build a decision support system for emergency response to road traffic injuries in real-time. A cost-sensitive artificial neural network with a novel heuristic cost matrix has been used to build a classifier capable of predicting the injury severity of occupants involved in crashes. The proposed system was designed to be used by the medical services dispatchers to better assess the severity of road traffic injuries, and therefore to better decide the most appropriate emergency response. Taking into account that the nature of accidents may change over time due to several reasons, the system enables users to build an updated version of the prediction model based on the historical and newly reported accidents. A dataset of accidents that occurred over a 6-year period (2008-2013) has been used for demonstration purposes throughout this paper. The accuracy of the prediction model was 65%. The Area Under the Curve (AUC) showed that the generated classifier can reasonably predict the severity of road traffic injuries. Importantly, using the cost-sensitive learning technique, the predictor overcame the problem of imbalanced severity distributions which are inherent in traffic accident datasets.

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

Online Nuclear Emergency Response System (ONERS) is a Decision Support System (DSS) developed for the management of Off-Site Nuclear Emergencies for Nuclear Power Plant sites of India. In this work, ONERS is incorporated with a module ‘Time-Dependent Source term (TDS)' for real-time online plume dispersion and radiological consequence assessments using time-varying source terms which is likely during an accidental release. TDS facilitates online dispersion and dose calculations by initiating the FLEXPART particle dispersion model with WRF predicted real-time meteorological forecasts and inputs on hourly release rates of 25 significant radionuclides of both ground level and elevated releases for any reactor accident over a 72-h meteorological forecast domain. The TDS is tested against the existing uniform source term approach for a hypothetical accident for two types of frequent weather conditions over Kalpakkam nuclear site in southern India. Simulations for a complex weather condition (monsoon & local sea breeze) during 26–29 September 2019 indicate that though the simulated plume area is similar in both the cases, the area affected is different as governed by the meteorological condition during the period of releases. It is found that the area and direction of potentially affected zone is determined by most frequent wind flow in the case of uniform source term and the flow direction during the period of maximum release in the case of time-dependent source term. Simulations for winter weather condition during 10–13 January 2020 with nearly uniform flow indicate similar results in both uniform and time-varying release cases. Results indicate that while the time-dependent source terms provide more realistic projection of radiological impact under complex meteorological conditions over uniform source term, both approaches would nearly converge when the maximum release occurs under the most frequent windflow condition. The two approaches are complementary to each other for response actions given the requirements of rapid assessments with gross source terms in the initial phases and more refined assessments with detailed source terms in the later phases of emergencies.

Intelligent decision support system for transportation emergency response

Continuous development of transport infrastructure raises requirements for transport safety. Road transport is the most emergency-prone and leading in the number of tragic consequences as well as financial and physical damage. Many accidents transform into emergencies (these include accidents with fatalities of at least 5 people or with the number of injured of at least 10 people). Damage from emergencies depends on the quality of managerial decisions taken at the response of rescue services. To solve the problem of taking qualitative managerial decisions, it is suggested to create an intelligent system based on expert knowledge and analysis of data from multiple precedents. It is suggested to use an artificial neural network that would solve the classification problem in the assessment of the scale of an accident or emergency. The decision on the number of emergency response manpower and resources and their distribution for accident or emergency response is made on the basis of such assessment.

DEVELOPMENT OF MOBILE DEVICE FOR GAMMA RADIATION MEASUREMENT UTILIZING LORA AS THE COMMUNICATION MEANS

Public protection is one of important issues when operating nuclear facility. In case of accident occurs, the facility owner and related organizations shall make decision whether to evacuate people or not, based on the level of the accident and radiation dose rate released to the environment. In this study, as part of the decision support system for nuclear emergency response, a prototype of mobile radiation measurement system has been developed. The device consists of Geiger-Muller (GM)-based radiation measurement board, Global Positioning System (GPS) module, microcontroller board, and low power LoRa module for communication. Radiation dose rate along with its geoposition were recorded and sent to base station equipped with LoRa gateway for connecting LoRa network to TCP/IP-based network. The measurement data is then published to storage server using Message Queuing Telemetry Transport (MQTT) protocol. Power consumption, measurement of counter/timer accuracy, communication ranges testing, and radiation dose rate measurement were performed around Puspiptek area to demonstrate the functionality of the system.Keywords: Radiation monitoring, Decision Support System, Mobile, LoRa, GPS

THE STUDY OF ATMOSPHERIC DISPERSION MODEL ON ACCIDENT SCENARIO OF RESEARCH REACTOR G. A. SIWABESSY USING HOTSPOT CODES AS A NUCLEAR EMERGENCY DECISION SUPPORT SYSTEM

G.A. Siwabessy Multipurpose Reactor (RSG-GAS) is a research reactor with thermal power of 30 MW located in the Serpong Nuclear Area (KNS), South Tangerang, Banten, Indonesia. Nuclear emergency preparedness of RSG-GAS needs to be improved by developing a decision support system for emergency response. This system covers three important aspects: accident source terms estimation, radioactive materials dispersion model into the atmosphere and radiological impact visualization. In this paper, radioactive materials dispersion during design basis accident (DBA) is modeled using HotSpot, by utilizing site-specific meteorological data. Based on the modelling, maximum effective dose and thyroid equivalent dose of 1.030 mSv and 26 mSv for the first 7 days of exposure are reached at distance of 1 km from the release point. These values are below IAEA generic criteria related to risk reduction of stochastic effects. The results of radioactive dispersion modeling and radiation dose calculations are integrated with Google Earth Pro to visualize radiological impact caused by a nuclear accident. Digital maps of demographic and land use data are overlayed on Google Earth Pro for more accurate impact estimation to take optimal emergency responses.Keywords: G.A. Siwabessy research reactor, Nuclear emergency, Atmospheric dispersion model, Decision support system, HotSpot codes

GIS-based Decision-Support System Applications in Disaster Management

Effective decision-making in disaster requires new tools such as geographic information system (GIS). This study examines how to use GIS tools disaster management organizations and do they have GIS-based decision support system. Furthermore it investigates whether organizations can collaborate with other organizations and what are the challenges during the crises or disaster events. We found that respondents in the survey indicated that all of their organization related to disaster management in Marmara region in Turkey use GIS and they strongly agree that their organization has a disaster and emergency response plan. They agree that emergency response is challenging because of criticality of the task itself and limited response time. Limited time and time pressure is the two major challenges of disaster management because each planning action considered at the ordinary time. But in times of crisis, unplanned situations may occur. That's why disaster management needs to GIS-based emergency response planning and decision support system.

A decision support system for emergency response to groundwater resource pollution in an urban area (Ljubljana, Slovenia)

Groundwater is an important resource for drinking water. Although it is a relatively stable and protected water resource, its quality, especially in highly urbanized catchments, is vulnerable to pollution related to unusual events or accidents causing releases of hazardous substances. To ensure the safe supply of drinking water in these cases, prompt actions must be taken and solutions must be delivered under time constraints. This paper describes a decision support system for emergency groundwater management that was developed to improve activities after the discovery of pollution in the catchments of drinking water abstraction wells in the Ljubljansko polje aquifer. It is a system based on a monitoring network and the integration of numerical modeling techniques with expert knowledge. The integration of logically interlinked activities, including the detection of pollution in the groundwater, the simulation of pollution propagation and decision making, into a common system provides a basis for proactive water resource management. The user-friendly interface enables water managers to utilize modeling tools and rapidly access information for mitigating pollution of groundwater resource, which in the presented case significantly contributes to a safer drinking water supply.

Exploring the potential of optical remote sensing for oil spill detection in shallow coastal waters--a case study in the Arabian Gulf.

Remote sensing provides an effective tool for timely oil pollution response. In this paper, the spectral signature in the optical and infrared domains of oil slicks observed in shallow coastal waters of the Arabian Gulf was investigated with MODIS, MERIS, and Landsat data. Images of the Floating Algae Index (FAI) and estimates of sea currents from hydrodynamic models supported the multi-sensor oil tracking technique. Scenes with and without sunglint were studied as the spectral signature of oil slicks in the optical domain depends upon the viewing geometry and the solar angle in addition to the type of oil and its thickness. Depending on the combination of those factors, oil slicks may exhibit dark or bright contrasts with respect to oil-free waters. Three oil spills events were thoroughly analyzed, namely, those detected on May 26 2000 by Landsat 7 ETM + and MODIS/Terra, on October 21 2007 by MERIS and MODIS, and on August 17 2013 by Landsat 8 and MODIS/Aqua. The oil slick with bright contrast observed by Landsat 7 ETM + on May 26 2000 showed lower temperature than oil-free areas. The spectral Rayleigh-corrected reflectance (R(rc)) signature of oil-covered areas indicated higher variability due to differences in oil fractions while the R(rc) spectra of the oil-free area were persistent. Combined with RGB composites, FAI images showed potentials in differentiating oil slicks from algal blooms. Ocean circulation and wind data were used to track oil slicks and forecast their potential landfall. The developed oil spill maps were in agreement with official records. The synergistic use of satellite observations and hydrodynamic modeling is recommended for establishing an early warning and decision support system for oil pollution response.

Risk Assessment Model of Information Security for Transportation Industry System Based on Risk Matrix

Risk assessment is a vital part of classification protection system for tran sportation industry system information security. In this paper, an information security risk assessment model based on risk matrix was put forward for heterogeneous-based transportation industry system. The risk matrix method was brought into assessment system. Then an accurate risk assessment model was constructed. In this model, there were three critical modules, such as expert 2-dimension matrix, Borda sequence and gray analytical hierarchy process. In this assessment system, qualitative analysis was firstly pro cessed. Those three modules have quantitative feature, so the assessment results described by words before can be denoted by numbers. The final quantitative results were obtained by related equations. This model was put into practical application of transportation s ecurity decision support system for emergency response, and the application results indicate that this model can increase the objectivity of th e assessment.

GIS-Based Emergency Management System for Chemical Industry Park

The chemical industrial park’s quick development has brought out many security and environment problem. For the common leakage accidents, it is urgently needed to build emergency response decision support system. The analysis of the accident is the crucial part of the system, in this paper the method of integrating GIS with diffusion model was used to simulate the three-dimensional diffusion process and predict the impact scope of the accident. The Pasquill-Gifford model and Gaussian diffusion model was introduced; Gaussian plume model was applied through the example to illustrate the method. The system is not just convenient for drawing up critical situation pre-arranged planning, but could offer timely assistant decision for emergency command.

An emergency response decision support system framework for application in e-government

An emergency response decision support system (ERDSS) needs to assist decision makers to evaluate emergency plans and select an appropriate one during an emergency by supporting heterogeneous emergency response data sources and providing decision makers the access to appropriate emergency rescue knowledge. It also needs to provide differentiated services to meet requirements. Whether the system is effective or not depends on the framework it is based on. This study proposes an ERDSS framework that consists of ten functional modules: Emergency Service Helpdesk, Command and Coordination Center, Emergency Plan Management, Emergency Relief Supplies Management, Emergency Finance Budget Management, Emergency Organization and Activity Management, Emergency Knowledge Warehouse, Emergency Alarm Management, and Problem Analysis and Management. A case study is conducted on Shanghai emergency management decision support system, an EMDSS based on this framework and applied currently in Shanghai, China. The study demonstrates that the proposed ERDSS system framework can provide theoretical and practical guidance for designing and developing effective emergency response systems.

A GIS-based generic real-time risk assessment framework and decision tools for chemical spills in the river basin

This paper presents a generic framework and decision tools of real-time risk assessment on Emergency Environmental Decision Support System for response to chemical spills in river basin. The generic “4-step-3-model” framework is able to delineate the warning area and the impact on vulnerable receptors considering four types of hazards referring to functional area, societal impact, and human health and ecology system. Decision tools including the stand-alone system and software components were implemented on GIS platform. A detailed case study on the Songhua River nitrobenzene spill illustrated the goodness of the framework and tool Spill first responders and decision makers of catchment management will benefit from the rich, visual and dynamic hazard information output from the software.

Assimilation of conventional and satellite wind observations in a mesoscale atmospheric model for studying atmospheric dispersion

A mesoscale atmospheric model PSU/NCAR MM5 is used to provide operational weather forecasts for a nuclear emergency response decision support system on the southeast coast of India. In this study the performance of the MM5 model with assimilation of conventional surface and upper-air observations along with satellite derived 2-d surface wind data from QuickSCAT sources is examined. Two numerical experiments with MM5 are conducted: one with static initialization using NCEP FNL data and second with dynamic initialization by assimilation of observations using four dimensional data assimilation (FDDA) analysis nudging for a pre-forecast period of 12 h. Dispersion simulations are conducted for a hypothetical source at Kalpakkam location with the HYSPLIT Lagrangian particle model using simulated wind field from the above experiments. The present paper brings out the differences in the atmospheric model predictions and the differences in dispersion model results from control and assimilation runs. An improvement is noted in the atmospheric fields from the assimilation experiment which has led to significant alteration in the trajectory positions, plume orientation and its distribution pattern. Sensitivity tests using different PBL and surface parameterizations indicated the simple first order closure schemes (Blackadar, MRF) coupled with the simple soil model have given better results for various atmospheric fields. The study illustrates the impact of the assimilation of the scatterometer wind and automated weather stations (AWS) observations on the meteorological model predictions and the dispersion results.

A Case Study of QR Decision Support System and Postponement Production in the Korean Apparel Company

The quick response(QR) system is very popular in Korean apparel companies. However, the usage of QR system was not known well. The purpose of this study is to identify the usage of the quick response decision support system(QR DSS) and postponement manufacturing in the Korean apparel company. The researched company was the only one which used the QR DSS. The researchers carried out the depth interview with the QR decision makers of the company. This company had 14 brands, and had used the QR DSS since January, 2008. The results are as follows: The QR DSS was supportive computer software program, and it helped the staffs to make agile decision about QR repeat production of clothing. The QR DSS automatically calculated the related data, and suggested the expected sales volume and the proper supply amounts of the styles. There were four functions in QR DSS : 'QR Alert', 'Proper Supply Amount Simulation', 'Sensible QR', and 'Supply/Sales Simulation by Item'. The men's clothing brands effectively used 'Supply/Sales Simulation by Item' function. And the women's clothing brands effectively used 'QR Alert' function. This company also used the postponement production system for QR repeat production. The postponement production was conducted with four methods : the yarn stocking, the grey fabric stocking, the dyed fabric stocking, and the fabric sourcing. The men's clothing brands usually used of the yarn stocking methods and the dyed fabric stocking methods. The women's clothing brands usually used the grey fabric stocking methods. By using QR DSS and postponement production system the company was able to shorten the lead time for QR decision making.

Transportation security decision support system for emergency response: A training prototype

During emergencies, decision making is a challenging task requiring immediate and effective action from responders under the pressures of incomplete and erroneous information. Identification of appropriate resources and personnel, proper lines of communication, and timely accessibility to relevant procedures can minimize after effects. To achieve emergency response and recovery effectiveness, responders need to be prepared and trained for various emergency situations and decision support systems. To address some of the decision making needs experienced by responders, a low-cost computer computer-based training prototype with a decision support system tool was developed. The emergency training prototype was designed for the Indiana Department of Transportation. Emergency responders' capabilities, collaboration with other agencies, deployment of resources and personnel, implementation of response plans, and use of the chain of command were evaluated. The usefulness of prototype and potential decision making systems for the transportation agency are validated based on several mock drills.