• Water Engineering Research
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• v.4 no.4
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• pp.187-192
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• 2003

The rainfall observation systems have largely been improved in Japan. The Japan Meteorological Agency, prefecture governments, and other administrative bodies have also increased the number of rain gauges thru out the country. The density of observatories is now one per several $\km^2$. Heavy rainfall information systems have been improved. Besides it, the Internet was popularized in the late 1990s, and has been used to transmit data of heavy rainfall. Internet accessible cellular phones have been popular in Japan since 1999. Such phones are expected to be useful in the field of disaster warning announcements, because they can automatically notify users bye-mail of pending disasters. The use of the Internet during natural disasters is groundbreaking in Japan today. However, in order to use disaster information effectively on Internet it is necessary to investigate how to use the information during the rainfall disaster. Therefore in our study we suggest methods on the effective construction and their use of information technology on Internet.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.851-859
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• 2019

The COMS satellites take image of the Korean Peninsula every 15 minutes, but due to the limitations of the observational channels, they tend to underestimate when estimating rainfall. In this study, we developed satellite-based rainfall estimation technology using COMS and GPM that can be used in the heavy rain on the Korean Peninsula. The time resolution and spatial resolution of COMS satellites and GPM satellites were matched to improve accuracy using GPM IMERG data. As a result, it showed that the number of correlations with the ASOS observations was more than 0.7, enabling the estimation of rainfalls that are more accurate than the estimates of rainfall by COMS satellites. It is believed that the application of the subsequent satellite(GK-2A) will provide more accurate rainfall estimation information in the future. Therefore, we expect greater utilization in disaster management for the ungauged areas.

• Journal of Environmental Science International
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• v.15 no.11
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• pp.1003-1010
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• 2006

It is very difficult to forecast accurately a damage from the natural disasters which occurs frequently. If the significant weather event was forecasted one or two days ago, we will be able to minimize a damage from the severe weather event through the suitable prevention activities. It said that 2000's our country's total damages from the meteorological disasters was several trillion won(Park et al, a, b, 2005). Therefore, we analyzed the Korea Meteorological Administration(KMA) and television broadcasting's reports, information contents, and transmission system, an ex post facto valuation about typhoon Nabi which struck the Korean peninsula from September 5 to 7, 2005. Through these investigations, we want to present the basic data to rises the application effect of disaster prevention meteorological information. We think KMA must present many information report to promote a citizen's understanding about the meteorological information and the serious disaster situation. And also we think the KMA and television broadcasting must present an advisable reports, the contents which is suitable to disaster response stages. And we must grasp the problem of disaster prevention meteorological information through an ex post facto examination, improve it effectively.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.12-22
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• 2018

In order to improve the vulnerability of current cities due to climate change, the disaster vulnerability analysis manual for various disasters is provided. Depending on the spatial units, the disaster vulnerability levels, and the conditions of the climatic factors, the results of the disaster vulnerability analysis will have a significant impact. In this study, relative assessments are conducted by adding the eup, myeon and dong unit in addition to census output area unit to analyze the impact on the spatial unit, and relative changes are analyzed according to the classification stages by expanding the natural classification, which is standardized at level four stage, to level two, four and six stage. The maximum rainfalls(10min, 60min, 24hr) are added for the two limited rainfall characteristics to determine the relativity of disaster vulnerable districts by index. The relative assessment results of heavy rainfall vulnerability index showed that the area ratio of disaster areas by spatial unit was different and the correlation analysis showed that the space analysis between the eup, myeon and dong unit in addition to census output area unit was not consistent. And it can be seen that the proportion of disaster vulnerable districts is relatively different a lot due to indexes of rainfall characteristics, spatial unit analysis and disaster vulnerability level stage. Based on the above results, it can be seen that the ratios of disaster vulnerable districts differ relatively significantly due to the level of the disaster vulnerability class, and the indexes of rainfall characteristics. This suggests that the impact of the disaster vulnerable districts depending on indexes is relatively large, and more detailed indexes should be selected when setting up the disaster vulnerabilities analysis index.

• Journal of Korean Society of Disaster and Security
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• v.15 no.4
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• pp.87-98
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• 2022

In recent years, frequent localized heavy rainfalls, which have a lot of rainfall in a short period of time, have been increasingly causing flooding damages. To prevent damage caused by localized heavy rainfalls, Hydrological Quantitative Precipitation Forecast (HQPF) was developed using the Local ENsemble prediction System (LENS) provided by the Korea Meteorological Administration (KMA) and Machine Learning and Probability Matching (PM) techniques using Digital forecast data. HQPF is produced as information on the impact of heavy rainfall to prepare for flooding damage caused by localized heavy rainfalls, but there is a tendency to overestimate the low rainfall intensity. In this study, we improved HQPF by expanding the period of machine learning data, analyzing ensemble techniques, and changing the process of Probability Matching (PM) techniques to improve predictive accuracy and over-predictive propensity of HQPF. In order to evaluate the predictive performance of the improved HQPF, we performed the predictive performance verification on heavy rainfall cases caused by the Changma front from August 27, 2021 to September 3, 2021. We found that the improved HQPF showed a significantly improved prediction accuracy for rainfall below 10 mm, as well as the over-prediction tendency, such as predicting the likelihood of occurrence and rainfall area similar to observation.

• Journal of the Korean earth science society
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• v.36 no.5
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• pp.460-475
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• 2015

A number of heavy rainfall events on the Korean Peninsula are indirectly influenced by tropical cyclones (TCs) when they are located in southeastern China. In this study, a heavy rainfall case in the middle Korean region is selected to examine the influence of typhoon simulation performance on predictability of remote rainfall over Korea as well as direct rainfall over Taiwan. Four different numerical experiments are conducted using Weather Research and Forecasting (WRF) model, toggling on and off two different improvements on typhoon in the model initial condition (IC), which are TC bogussing initialization and dropwindsonde observation data assimilation (DA). The Geophysical Fluid Dynamics Laboratory TC initialization algorithm is implemented to generate the bogused vortex instead of the initial typhoon, while the airborne observation obtained from dropwindsonde is applied by WRF Three-dimensional variational data assimilation. Results show that use of both TC initialization and DA improves predictability of TC track as well as rainfall over Korea and Taiwan. Without any of IC improvement usage, the intensity of TC is underestimated during the simulation. Using TC initialization alone improves simulation of direct rainfall but not of indirect rainfall, while using DA alone has a negative impact on the TC track forecast. This study confirms that the well-suited TC simulation over southeastern China improves remote rainfall predictability over Korea as well as TC direct rainfall over Taiwan.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.101-110
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• 2010

The increased occurrence of flooding due to typhoons and local rainfall has necessitated damage prevention through the systematic construction of damage history and quantitative analysis of flood prediction data. In this study, we constructed a disaster information map for practical use by combining digital images and continuous cadastral maps of damaged areas using a geographic information system to provide basic data and attribute information. In addition, we predicted the areas at risk of flash floods by calculating the flood capacity of the study area for different rainfall frequencies through flood inundation simulation, which was used to obtain comprehensive disaster information. Further, we calculated the extent of the flooded area and the damage rate for different rainfall frequencies using cadastral information. Flood inundation simulation in the case of heavy rainfall was found to help improve the ability to react to a flood and enhance the efficiency of rescue work by supporting decision-making for disaster management.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.313-318
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• 2006

The main cause of natural disaster in Korea is meteorological phenomenon, such as typhoon, heavy rain, storm, rainstorm, heavy snow, hailstorm, overflowing of sea and so on(including thunderstroke, blast, snow damage, freezing and earthquake), and among those disasters, heavy rain takes place most often, and it occupies 80% of total disaster Especially, disaster related to slope collapse (landslide, collapse of retaining wall, burying ect.) takes place every year due to meteorological cause such as localized heavy rain, which is getting stronger. (National Institute for Prevention Disaster, 2002, Meteorological Administration) Accordingly, it is necessary to analyze the features of slope collapse related to natural disaster in Korea, and also to make up counterplan to prevent disaster. This paper will try to analyze potential areas which are susceptible to landslide regarding factors inducing landslide and heavy rain, and to evaluate the potentiality of landslide regarding local particularity of rainfall, furthermore to provide essential information for development of community such as preventing damages from landslide, construction Industry, and effective use of land.

• Journal of Environmental Science International
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• v.28 no.1
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• pp.125-135
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• 2019

For the purposes of enhancing usability of Numerical Weather Prediction (NWP), the quantitative precipitation prediction scheme by machine learning has been proposed. In this study, heavy rainfall was corrected for by utilizing rainfall predictors from LENS and Radar from 2017 to 2018, as well as machine learning tools LightGBM and XGBoost. The results were analyzed using Mean Absolute Error (MAE), Normalized Peak Error (NPE), and Peak Timing Error (PTE) for rainfall corrected through machine learning. Machine learning results (i.e. using LightGBM and XGBoost) showed improvements in the overall correction of rainfall and maximum rainfall compared to LENS. For example, the MAE of case 5 was found to be 24.252 using LENS, 11.564 using LightGBM, and 11.693 using XGBoost, showing excellent error improvement in machine learning results. This rainfall correction technique can provide hydrologically meaningful rainfall information such as predictions of flooding. Future research on the interpretation of various hydrologic processes using machine learning is necessary.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.191-201
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• 2018

Recently, the frequency of heavy rainfall is increasing due to the effects of climate change, and heavy rainfall in urban areas has an unexpected and local characteristic. Floods caused by localized heavy rains in urban areas occur rapidly and frequently, so that life and property damage is also increasing. It is crucial how fast and precise observations can be made on successful flood management in urban areas. Local heavy rainfall is predominant in low-level storms, and the present large-scale radars are vulnerable to low-level rainfall detection and observations. Therefore, it is necessary to introduce a new urban flood forecasting system to minimize urban flood damage by upgrading the urban flood response system and improving observation and forecasting accuracy by quickly observing and predicting the local storm in urban areas. Currently, the WHAP (Water Hazard Information Platform) Project is promoting the goal of securing new concept water disaster response technology by linking high resolution hydrological information with rainfall prediction and urban flood model. In the WHAP Project, local rainfall detection and prediction, urban flood prediction and operation technology are being developed based on high-resolution small radar for observing the local rainfall. This study is expected to provide more accurate and detailed urban flood warning system by enabling high-resolution observation of urban areas.