• Journal of Information Technology Applications and Management
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• v.17 no.3
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• pp.163-189
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• 2010

The purpose of this research is to develop a decision-making model for the calculation of the National Disaster Management System's standard prevention workforce quota. The final purpose of such model is to support in arranging a rationally sized prevention workforce for local authorities by providing information about its calculation in order to support an effective and efficient disaster management administration. In other words, it is to establish and develop a model that calculates the standard disaster prevention workforce quota for basic local governments in order to arrange realistically required prevention workforce. In calculating Korea's prevention workforce, it was found that the prevention investment expenses, number of prevention facilities, frequency of flood damage, number of disaster victims, prevention density, and national disaster recovery costs have positive influence on the dependent variable when the standard prevention workforce was set as the dependent variable. The model based on the regression analysis-which consists of dependent and independent variables-was classified into inland mountainous region, East coast region, Southwest coastal plain region to reflect regional characteristics for the calculation of the prevention workforce. We anticipate that the decision-making model for the standard prevention workforce quota will aid in arranging an objective and essential prevention workforce for Korea's basic local authorities.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.315-318
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• 2007

In order to reduce the amount of damage from natural disasters, we needs prevention meteorological database classified into the cause of disaster, damage elements etc. For this, we have analyzed four data, such as Statistical yearbook of calamities issued by the National Emergency Management Agency and Annual Climatological Report issued by the Korea Meteorological Administration and Recently 10 years for natural disaster damage and Statistics Yearbook from the Ministry of Government Administration and Human affairs. Through the analysis of disaster data, we have selected input variables, such as causes and elements, occurrence frequencies, vulnerable areas of natural disaster, etc. In order to reduce damage from natural disaster, the prevention activities and forecasting based on meteorological parameters and damage datas are required. In addition, it is necessary to process meteorological information for disaster prevention activities. Through these procedure, we have established the foundation of database about natural disasters. This database will be used to assess the natural disasters and build risk model and natural disasters mitigation plan.

• Journal of the Society of Disaster Information
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• v.7 no.4
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• pp.294-304
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• 2011

Disaster prevention resources consist of persons, equipments, goods, and facilities. Specially, equipments and goods are internally managed in the regional office, and may be leased to the inter-offices. National Disaster Management System(NDMS) managed the resources in the separated system. That is, one system is managed by regional disaster prevention system, the other is managed by the central disaster prevention system which receives the data from the regional disaster prevention system. Because the regional disaster prevention system updates the resource information quarterly, it is difficult to confirm the exact information. In this study, we propose the model which makes to supply in timely the resource to the disaster using the managed resource with RFID technology. Firstly, we classified the resources based on the resource types for the easily search. After then, we defined ID and attributes information of the resources. From the information, we described the management model for the real time resource.

• Journal of the Korean Society of Hazard Mitigation
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• v.7 no.3
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• pp.41-49
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• 2007

In order to reduce the amount of damage from natural disasters and perform the natural disaster mitigation program, the prevention activities and forecasting based on meteorological parameters and disaster datas are required. In addition, it is necessary to process prevention meteorological information for prevention activities in advance. For this, we have analyzed four data, such as Statistical yearbook of calamities and Statistics Yearbook issued by the Ministry of Government Administration and Human affairs. And Annual Climatological Report issued by the Korea Meteorological Administration and Recently 10 years for natural disaster damage from the Central Disaster and Safety Countermeasures Headquarters. We analyzed the causes, elements, occurrence frequencies, and vulnerable areas of natural disaster, using the 4 disaster datas, but these datas was not consistent with their terminology and items. Through the analysis of a kind and damage of disaster, we have selected the disaster variables, such as causes and elements, the amount of damage, vulnerable areas of natural disaster, etc and made a database. This database will be used to assess the natural disasters and develop the risk model and natural disasters mitigation plan.

• Journal of the Society of Disaster Information
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• v.16 no.3
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• pp.493-504
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• 2020

Purpose: The purpose of this study is to propose an analytical methodology for selecting the priority of preventive projects in the course of carrying out disaster prevention projects that improve disaster-hazardous areas. Method: Data analysis was performed using RFM model which can divide data grade and perform target marketing based on Recency, Frequency, and Monetary. Result: The top 10% of the area with high RFM value was mainly in the East Sea and the South Sea coast, and the number of damage in private facilities was high. Conclusion: In this study, we used the RFM model to select the priority of disaster risk and to implement the regional disaster risk using GIS. These results are expected to be used as basic data for selecting priority project sites for disaster prevention projects and as basic data in the decision-making process for disaster prevention projects.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.127-130
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• 2008

This study demonstrates a basic concept and designing direction for the realisation of disaster prevention planning in u-City(u-BangjaeCity) that is from establishing a planning system in terms of the areas of professional disaster and safety management for the national disaster management. and designing disaster or safety management system via using ubiquitous technology for the scientific disaster management. In order to realise u-City it is necessary to maps out interrelation amongst various services such as traffic, environment and disaster prevention. Domestic and international case studies regarding the tendency of disaster prevention planing in u-City and its analysis could be the fundamental resource in order to develop the standard model of u-BangjaeCity.

• Disaster Prevention Review
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• v.14 no.4
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• pp.124-125
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• 2012

• Journal of the Society of Disaster Information
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• v.15 no.4
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• pp.502-513
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• 2019

Purpose: This study intends to review the procedures for the establishment of the plan for disaster mitigation activities and the system for the management of disaster mitigation activities for the requirements of the enterprise disaster management standard. Method: The requirements, including the activities of each stage of disaster management defined within the scope of the corporate disaster management standard, were identified, the operational cases of 'A' institution were reviewed, and the targets of continuous planning were reviewed to meet the requirements. Result: It was reviewed that the contents and procedures of each phase of disaster management, which is a requirement of the enterprise disaster management standard, are clearly defined, and a task continuity plan is established for each stage of activity. Conclusion: Conclusion : The PDCA model including the activities of each stage of disaster management activities was presented for the requirements of the enterprise disaster management standard, and the disaster mitigation activity management system model of the broad concept of disaster management including prevention and preparedness plans for disasters was presented.

• International journal of steel structures
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• v.18 no.4
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• pp.1153-1166
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• 2018

This paper was concerned with the nonlinear analysis on the overall stability of H-type honeycombed composite column with rectangular concrete-filled steel tube flanges (STHCC). The nonlinear analysis was performed using ABAQUS, a commercially available finite element (FE) program. Nonlinear buckling analysis was carried out by inducing the first buckling mode shape of the hinged column to the model as the initial imperfection with imperfection amplitude value of L/1000 and importing the simplified constitutive model of steel and nonlinear constitutive model of concrete considering hoop effect. Close agreement was shown between the experimental results of 17 concrete-filled steel tube (CFST) specimens and 4 I-beams with top flanges of rectangular concrete-filled steel tube (CFSFB) specimens conducted by former researchers and the predicted results, verifying the correctness of the method of FE analysis. Then, the FE models of 30 STHCC columns were established to investigate the influences of the concrete strength grade, the nominal slenderness ratio, the hoop coefficient and the flange width on the nonlinear stability capacity of SHTCC column. It was found that the hoop coefficient and the nominal slenderness ratio affected the nonlinear stability capacity more significantly. Based on the results of parameter analysis, a formula was proposed to predict the nonlinear stability capacity of STHCC column which laid the foundation of the application of STHCC column in practical engineering.

• Journal of Korea Water Resources Association
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• v.52 no.12
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• pp.963-973
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• 2019

This study reviews a urban flooding risk criteria estimation model to predict risk criteria in areas where flood risk criteria are not precalculated by using watershed characteristic data and limit rainfall based on damage history. The risk criteria estimation model was designed using Support Vector Machine, one of the machine learning algorithms. The learning data consisted of regional limit rainfall and watershed characteristic. The learning data were applied to the SVM algorithm after normalization. We calculated the mean absolute error and standard deviation using Leave-One-Out and K-fold cross-validation algorithms and evaluated the performance of the model. In Leave-One-Out, models with small standard deviation were selected as the optimal model, and models with less folds were selected in the K-fold. The average accuracy of the selected models by rainfall duration is over 80%, suggesting that SVM can be used to estimate flooding risk criteria.