• Proceedings of the Safety Management and Science Conference
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• 2005.05a
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• pp.31-38
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• 2005

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 2004.04a
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• pp.425-429
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• 2004

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.2
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• pp.1-10
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• 2009

Recently the large scale of forest disaster such as landslide and forest fire gives a very bad impact on not only forest ecosystem but also farm business so that it has became the main issue of environmental problems. In this study, the landslide hazard area forecast method was developed by considering not only the topographic thematic maps based on GIS and satellite images but also amount of rainfall data, which are very important factors of landslide. Uljin-gun was selected as the study area and the GIS weight score and overlay analysis were applied to topographical map and meteorological observation map. Finally the landslide area distribution map was constructed by considering the evaluation criteria. Also, the accuracy could be acquired by comparing the landslide hazard area forecast map and real damaged area extracted from satellite image.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.351-362
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• 2017

Recently, analysis techniques to extract new meanings using big data analysis and various services using them have been developed. A disaster safety service among such services has been paid attention as the most important service. In this paper, we design and implement a flood disaster safety system using real time weather big data. The proposed system retrieves and processes vast amounts of information being collected in real time. In addition, it analyzes risk factors by aggregating the collected real time and past data and then provides users with prediction information. The proposed system also provides users with the risk prediction information by processing real time data such as user messages and news, and by analyzing disaster risk factors such a typhoon and a flood. As a result, users can prepare for potential disaster safety risks through the proposed system.

• Journal of agriculture & life science
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• v.45 no.4
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• pp.29-35
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• 2011

A forest fire was one of the huge disasters and damaged human lifes and a properties. Therefore, many countries operated forest fire forecasting systems which developed from forest fire records, weather data, fuel models and etc. And many countries also estimated future state of forest fire using a long-term climate forecasting like GCMs and prepared resources for future huge disasters. In this study, we analyzed relationships between forest fire occurrence and meteorological factors (the minimum temperature ($^{\circ}C$), the relative humidity (%), the precipitation (mm), the duration of sunshine (hour) and etc.) for developing a estimating tools, which could forecast forest fire regime under future climate change condition. Results showed that forest fires in this area were mainly occurred when the maximum temperature was $10{\sim}200^{\circ}C$, when the relative humidity was 40~60%, and when the average wind speed was under 2m/s. And forest fires mainly occurred at 2~3 day after rainfall.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.358-358
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• 2021

자연재해 중 홍수는 전 세계적으로 가장 큰 인적 및 물적 피해를 발생시키고 있으며, 지구온난화로 가속화되고 있는 기후변화는 더욱 극심한 호우와 태풍 현상을 야기하고 있다. 최근 우리나라에서도 2020년 장마는 역대 가장 긴 장마로 기록되는 등 변화된 기상현상으로 인해 홍수피해의 빈도와 강도가 지속적으로 증가하고 있다. 따라서, 이상기후로 인한 홍수피해에 대한 대비와 적응을 위해 위험도 평가, 예·경보시스템, 대피체계 등과 같은 비구조적 대책의 수립이 필요하다. 그 중 홍수피해에 대한 위험도 평가는 과거 홍수피해자료를 바탕으로 지역별 피해양상이나 상대적인 피해위험도를 파악할 수 있으므로 홍수피해 저감대책 수립에 중요한 비구조적 도구로 인식되고 있다. 이에 따라 본 연구는 행정구역별 과거 강우특성 및 홍수피해자료를 분석하여 강우조건에 따라 예상되는 홍수피해위험도를 평가하는 방법을 제안하고자 한다. 이를 위해 먼저, 국민재난안전포털에서 제공하는 재해연보에서 행정구역별 최근 20년 동안의 호우 및 태풍으로 인한 피해자료를 수집하여 인적 및 물적 피해특성 자료를 구축하고, 홍수피해가 발생한 기간에 대해 기상청에서 제공하는 시강우량 자료를 수집하여 홍수피해 사상별 다양한 강우특성자료를 구축한다. 구축된 자료를 이용하여 행정구역별 강우-피해 상관분석을 수행하고, 회귀분석 과정에서 이상치가 존재할 경우 회귀모형의 적합도를 향상시키기 위해 이상치를 제거하고 분석하여, 회귀식의 결정계수 및 유의성 검정결과를 바탕으로 3가지 원인별(호우, 태풍, 종합), 2가지 홍수피해별(인적, 물적) 강우-피해 최적 회귀함수를 선정한다. 최종적으로 강우조건에 따른 홍수피해 규모를 예측하고, 이를 통하여 행정구역별 상대적인 홍수피해위험도를 평가한다. 본 연구를 통해 행정구역별 강우조건에 따른 예상 홍수피해위험도를 분석하여 홍수피해에 대한 저감대책 수립에 기초자료를 제공할 수 있을 것으로 기대된다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.

• Journal of Korea Water Resources Association
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• v.49 no.9
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• pp.749-759
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• 2016

The aim of this study is to apply and to evaluate the radar-based risk prediction algorithm for damage reduction by sudden localized heavy rain in urban areas. The algorithm is combined with three processes such as "detection of cumulonimbus convective cells that can cause a sudden downpour", "automatic tracking of the detected convective cells", and "risk prediction by considering the possibility of sudden downpour". This algorithm was applied to rain events that people were marooned in small urban stream. As the results, the convective cells were detected through this algorithm in advance and it showed that it is possible to determine the risk of the phenomenon of developing into local heavy rain. When use this risk predicted results for flood prevention operation, it is able to secure the evacuation time in small streams and be able to reduce the casualties.

• Journal of the Korean earth science society
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• v.43 no.1
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• pp.30-40
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• 2022

Wildfires, which occur sporadically and irregularly worldwide, are distinct natural disturbances in combustible vegetation areas, important parts of the global carbon cycle, and natural disasters that cause severe public emergencies. While many previous studies have investigated the variability and changes in wildfires globally based on fire emissions, burned areas, and fire weather indices, studies on East Asia are still limited. Here, we explore the characteristics of variability and changes in wildfire danger over East Asia by analyzing the fire weather index for the 40 years-1981-2020. The first empirical orthogonal function (EOF) mode of fire weather index variability represents an increasing trend in wildfire danger over most parts of East Asia over the last 40 years, accounting for 29% of the total variance. The major contributor is an increase in the surface temperature in East Asia associated with global warming and multidecadal ocean variations. The effect of temperature was slightly offset by the increase in soil moisture. The second EOF mode exhibits considerable interannual variability associated with the El Nino-Southern Oscillation, accounting for 17% of the total variance. The increase (decrease) in precipitation in East Asia during El Nino (La Nina) increases (decreases) soil moisture, which in turn reduces (increases) wildfire danger. This dominant soil moisture effect was slightly offset by the temperature increase (decrease) during El Nino (La Nina). Improving the understanding of variability and changes in wildfire danger will have important implications for reducing social, economic, and ecological losses associated with wildfire occurrences.

• Convergence Security Journal
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• v.13 no.3
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• pp.79-84
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• 2013

In this study the improved service innovation model to solve the problems that appear from a vantage point of the providing security services process through the application and appeal process of convergence security technologies proposed. The model was in view of service science to resolves the limitations that facilities management and unmanned security of physical security field through the application of meteorological information on convergence security technologies. The contribution of this research: improved risk management based on convergence security technologies through service innovation management, evaluated the quantitative value of risk management activity using service effects, and development of physical security service providing methodology using meteorological information.