• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.109-112
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• 2008

The purpose of this study is to find out the characteristics of disasters caused by typhoons passing through the sea area around the Korean Peninsula. It analyzed two cases, that is, in WEST and EAST cases. These include the typhoons passing through the Yellow Sea, west of the Peninsula and East Sea, east of the Peninsula without landing on the Peninsula. FCM (Fuzzy Clustering Method) analysis was performed on typhoons affecting the Korean Peninsula from 1951 to 2006. The analysis shows that WEST case's cluster has the curved track of NE-S, and EAST case's cluster has the straight track of NE-SW. Typhoons that pass through the Yellow Sea have little change in frequency and the weak intensity. On the other hand, the frequency and the intensity of typhoons passing through the East Sea show the increasing trend. The characteristic of disasters by typhoons affecting the Korean Peninsula from 1973 to 2006 appears differently for each case: EAST cases caused significant damage in flooding, while WEST cases did damage in houses, ships, roads, and bridges. Rainfall amount and maximum wind speed data are analyzed in order to understand the impact of the typhoons, and the result indicates that the WEST cases are influenced by the wind, and East cases by precipitation. The result of this study indicates that the characteristic of disasters is distinctive according to the Typhoon's track. If applied to establish the disaster prevention plan, this result could make a contribution to the damage reduction.

• Journal of Korea Water Resources Association
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• v.38 no.10 s.159
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• pp.825-839
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• 2005

Application of the EST approach for the simulation of the risk-based typhoon hazard potential is described in this paper. For six selected cities In the Korean peninsula, EST simulations for one hundred years were performed one hundred times using historical typhoon data as a training data set. The analytical results of EST simulations were then post-processed to estimate the means, standard deviations, and ranges of variation for the maximum wind velocities and the daily rainfalls. From the comparison of the averages of the wind velocities for the 100 year recurrence interval typhoons, the wind hazard potential of them was revealed to be highest for Mokpo among the six cities, followed by Busan, Cheju, Inchun, Taegu, and Seoul in descending order For the flood hazard potential associated with a typhoon, Busan was ranked to be the highest hazard potential area, followed by Mokpo, Cheju, Seoul, Inckun, and Taegu. In terms of the overall typhoon hazard potential, cities in the southern coastal regions were identified as being exposed to the most severe typhoon hazard.

• Journal of Korea Water Resources Association
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• v.53 no.4
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• pp.313-322
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• 2020

There is always a risk of water disasters due to sudden storms in mountainous regions in Korea, which is more than 70% of the country's land. In this study, a radar-based risk prediction technique for sudden downpour is applied in the mountainous region and is evaluated for its applicability using Mt. Biseul rain radar. Eight local heavy rain events in mountain regions are selected and the information was calculated such as early detection of cumulonimbus convective cells, automatic detection of convective cells, and risk index of detected convective cells using the three-dimensional radar reflectivity, rainfall intensity, and doppler wind speed. As a result, it was possible to confirm the initial detection timing and location of convective cells that may develop as a localized heavy rain, and the magnitude and location of the risk determined according to whether or not vortices were generated. In particular, it was confirmed that the ground rain gauge network has limitations in detecting heavy rains that develop locally in a narrow area. Besides, it is possible to secure a time of at least 10 minutes to a maximum of 65 minutes until the maximum rainfall intensity occurs at the time of obtaining the risk information. Therefore, it would be useful as information to prevent flash flooding disaster and marooned accidents caused by heavy rain in the mountainous area using this technique.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1085-1092
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• 2009

The ozone data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model and Neural Networks (NN) model have been considered for analyzing the ozone data at the northern part of the Gyeonggi-Do, Paju monitoring site in Korea. In the both ARE model and NN model, seven meteorological variables and four pollution variables are used as the explanatory variables for the ozone data set. The seven meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, dew point temperature, steam pressure, and amount of cloud. The four air pollution explanatory variables are Sulfur dioxide ($SO_2$), Nitrogen dioxide ($NO_2$), Cobalt (CO), and Promethium 10 (PM10). The result showed that the NN model is generally better suited for describing the ozone concentration than the ARE model. However, the ARE model will be expected also good when we add the explanatory variables in the model.