• Journal of Korea Water Resources Association
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• v.47 no.12
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• pp.1187-1198
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• 2014

Strong winds and heavy rainfall from tropical cyclones (TCs) that occur in the Northwestern Pacific cause significant human and material damage to the Korean peninsula and East Asia. Hence, it is important to establish early warning systems and conduct preparedness activities in advance of a TC. This study suggests a technique to extract the value of uniform TC-induced rainfall considering the TC track and TC size. To validate our technique, we compare it to existing TC rainfall techniques using the spatial domain. To determine the TC size required for extracting TC-induced rainfall, this research analyzed the mean of TC-induced rainfall by TC size (1973-2012). As a result of this analysis, the maximum amount of mean of TC-induced rainfall was found for a TC with a radius of 700 km. Other techniques have limitations which this new technique addresses; it can extract TC-induced rainfall in each administrative area and minimize systematic biases of other extraction methods. The result of this study can be utilized in the preparation of rainfall forecasts, designing hydraulic structures, and predicting landslide and debris flows using TC-induced rainfall and downpours.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.311-323
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• 2023

In order to reduce disaster damage by localized heavy rains, floods, and urban inundation, it is important to know in advance whether natural disasters occur. Currently, heavy rain watch and heavy rain warning by the criteria of the Korea Meteorological Administration are being issued in Korea. However, since this one criterion is applied to the whole country, we can not clearly recognize heavy rain damage for a specific region in advance. Therefore, in this paper, we tried to reset the current criteria for a special weather report which considers the regional characteristics and to predict the damage caused by rainfall after 1 hour. The study area was selected as Gyeonggi-province, where has more frequent heavy rain damage than other regions. Then, the rainfall inducing disaster or hazard-triggering rainfall was set by utilizing hourly rainfall and heavy rain damage data, considering the local characteristics. The heavy rain damage prediction model was developed by a decision tree model and a random forest model, which are machine learning technique and by rainfall inducing disaster and rainfall data. In addition, long short-term memory and deep neural network models were used for predicting rainfall after 1 hour. The predicted rainfall by a developed prediction model was applied to the trained classification model and we predicted whether the rain damage after 1 hour will be occurred or not and we called this as 1ST-Model. The 1ST-Model can be used for preventing and preparing heavy rain disaster and it is judged to be of great contribution in reducing damage caused by heavy rain.