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

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

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.14
• /
• pp.5571-5575
• /
• 2014

Background: The ability to predict the survival time of breast cancer patients is important because of the potential high morbidity and mortality associated with the disease. To develop a predictive inference for determining the survival of breast cancer patients, we applied a novel Bayesian method. In this paper, we propose the development of a databased statistical probability model and application of the Bayesian method to predict future survival times for White Hispanic female breast cancer patients, diagnosed in the US during 1973-2009. Materials and Methods: A stratified random sample of White Hispanic female patient survival data was selected from the Surveillance Epidemiology and End Results (SEER) database to derive statistical probability models. Four were considered to identify the best-fit model. We used three standard model-building criteria, which included Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) to measure the goodness of fit. Furthermore, the Bayesian method was used to derive future survival inferences for survival times. Results: The highest number of White Hispanic female breast cancer patients in this sample was from New Mexico and the lowest from Hawaii. The mean (SD) age at diagnosis (years) was 58.2 (14.2). The mean (SD) of survival time (months) for White Hispanic females was 72.7 (32.2). We found that the exponentiated Weibull model best fit the survival times compared to other widely known statistical probability models. The predictive inference for future survival times is presented using the Bayesian method. Conclusions: The findings are significant for treatment planning and health-care cost allocation. They should also contribute to further research on breast cancer survival issues.

• The Journal of the Korea Contents Association
• /
• v.21 no.5
• /
• pp.22-35
• /
• 2021

The unprecedented pandemic caused by the COVID-19 has led to a massive global public health campaign to slow the spread of the virus. Thus, this study examines the importance of individual's prevention behavior intention by adapting health belief model(HBM). In addition, we added social variables to understand the prevention behavior better considering the situation in which collective behaviors are important. The online survey results(N=298) showed that higher level of perceived severity, perceived susceptibility, perceived benefits, perceived peril, perceived social norms and lower level of perceived responsibility led to higher prevention behavior intention. Peril was the most influential factor among all the variables. In addition, perceived severity and social norms followed after that. Additional analysis also implied that socio-HBM model we proposed better explained the prevention behavior intention than traditional HBM.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
• /
• 2011.04a
• /
• pp.28-31
• /
• 2011

최근 산불과 같은 불티로 인한 2차화재의 발생 비율이 높아지고 있는 추세이다. 그러나 이를 불티로 인한 화재 성상을 CFD로 해석하기 위해서는 불티에 가해지는 유체력을 평가하여야 연소모델을 적용할 수 있다. 따라서 본 연구에서는 기존 풍속에 의한 불티 확산실험의 결과를 토대로 FDS에 적용 할 수 있는 연소모델을 구축하고 위하여 수리 모델 및 수치 해법에 대해 정리하고 이를 FDS 연소모델에 적용을 실시하였다.

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

• Journal of Korean Society on Water Environment
• /
• v.20 no.3
• /
• pp.223-230
• /
• 2004

The QUAL2E and Box-Jenkins time series model were applied to the Miho river, a main tributary of the Geum river, to predict water quality. The models are widely used to predict water quality in rivers and watersheds because of its accuracy. As results of the study, we concluded as follows: Pollutant loadings in upper stream of Miho river were determined to 57,811 kgBOD/d, 19,350 kgTN/d, and 5,013 kgTP/d. The loading of TN in Mushim river was 19,450 kgTN/d, respectively. As the mass loadings were compared with pollutant sources, it concluded that the farming livestock contributed highly to mass emissions of BOD and TP and the population contributed to TN mass loading. The observed water quality values were applied to the models to verify and the models were used to predict the water quality. The QUAL2E Model predicted the concentrations of DO, BOD, TN and TP with high accuracy, but not for E-Coli. The Box-Jenkins time series model also showed high prediction for DO, BOD and TN. However, the concentrations of TP and E-Coli were poorly predicted. The result shows that the QUAL2E model is more applicable in Miho basin for prediction of water quality compared to Box-Jenkins time series model.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.172-172
• /
• 2020

In recent years, soil erosion has come to be regarded as an essential environmental problem in human life. Soil erosion causes various on- and off-site problems such as ecosystem destruction, decreased agricultural productivity, increased riverbed deposition, and deterioration of water quality in streams. To solve these problems caused by soil erosion, it is necessary to quantify where, when, how much soil erosion occurs. Empirical erosion models such as the Universal Soil Loss Equation (USLE) family models have been widely used to make spatially distributed soil erosion vulnerability maps. Even if the models detect vulnerable sites relatively well by utilizing big data related to climate, geography, geology, land use, etc. within study domains, they do not adequately describe the physical process of soil erosion on the ground surface caused by rainfall or overland flow. In other words, such models remain powerful tools to distinguish erosion-prone areas at the macro scale but physics-based models are necessary to better analyze soil erosion and deposition and eroded particle transport. In this study, the physics-based Surface Soil Erosion Model (SSEM) was upgraded based on field survey information to produce sediment yield at the watershed scale. The modified model (hereafter MoSE) adopted new algorithms on rainfall kinematic energy and surface flow transport capacity to simulate soil erosion more reliably. For model validation, we applied the model to the Doam dam watershed in Gangwon-do and compared the simulation results with the USLE outputs. The results showed that the revised physics-based soil erosion model provided more improved and reliable simulation results than the USLE in terms of the spatial distribution of soil erosion and deposition.

• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.4
• /
• pp.2317-2323
• /
• 2013

The role of The Transtheoretical Model (TTM) in predicting relapse is limited. We aimed to assess whether this model can be utilised to predict relapse during the action stage. The participants included 120 smokers who had abstained from smoking for at least 24 hours following two Malaysian universities' smoking cessation programme. The smokers who relapsed perceived significantly greater advantages related to smoking and increasing doubt in their ability to quit. In contrast, former smokers with greater self-liberation and determination to abstain were less likely to relapse. The findings suggest that TTM can be used to predict relapse among quitting smokers.

• Journal of the Korean Society of Safety
• /
• v.20 no.1 s.69
• /
• pp.153-157
• /
• 2005

The injury accidents in fishing vessels account for $67.2\%$ of all marine injury casualties$(1997\~2001)$ and is on an increasing trend every year. Also, it is remarkable for the injury accidents to be basically caused by human errors. This study aims to investigate the human error of injury accidents in fishing vessels and presents the injury preventing program in them. Human errors were analysed by the methods such as SHELL & Reason Hybrid Model, GEMS Model adopted by International Maritime Organization(IMO). Based on the analysis, the following propositions were made to reduce the fishing vessels accidents by human errors : improvement of hazard awareness and quality of personnel, establishment of safety management system, and enforcement of vessels inspection.