• Journal of the Korea Institute of Building Construction
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• v.16 no.5
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• pp.437-445
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• 2016

Since typhoon is a critical meteorological disaster, some advanced countries have developed typhoon damage prediction models. However, although South Korea is vulnerable to typhoons, there is still shortage of study in typhoon damage prediction model reflecting the vulnerability of domestic building and features of disaster. Moreover, many studies have been only focused on the characteristics and typhoon and regional characteristics without various influencing factors. Therefore, the objective of this study is to analyze typhoon damage by path and develop to prediction model for building damage ratio by using multiple regression analysis. This study classifies the building damages by typhoon paths to identify influencing factors then the correlation analysis is conducted between building damage ratio and their factors. In addition, a multiple regression analysis is applied to develop a typhoon damage prediction model. Four categories; typhoon information, geography, construction environment, and socio-economy, are used as the independent variables. The results of this study will be used as fundamental material for the typhoon damage prediction model development of South Korea.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.79-87
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• 1998

정확한 태풍진로 예측은 동아시아 최대의 자연재해인 태풍의 피해를 최소화하는데 필수적이다. 기상역학에 기초를 둔 수치모델과 회귀분석등의 통계적 접근법이 사용되어왔다. 본 논문에서는 비선형 신경망모델인 다층퍼셉트론을 제안한다. 즉, 태풍진로예측을 이동경로, 속도, 기압 등의 변수로 이루어진 시계열의 예측으로 본다. 1945년부터 1989년까지 한반도에 접근한 태풍 데이터를 이용하여 제안된 신경망을 학습한 후, 94, 95년도에 접근한 태풍의 진로를 예측하였다. 신경망의 예측성능은 수치모델의 성능보다 조금 우수하거나 비슷하였다. 신경망의 성능은 충분히 더 향상될 수 있는 여지가 있다. 또한, 고가의 슈퍼컴퓨터로 여러 시간 계산을 해야하는 수치모델에 비하여 PC상에서 수초만에 계산을 할 수 있는 신경망 모델은 비용 면에서도 장점이 있다.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.35 no.4
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• pp.67-74
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• 2023

High waves and storm surges due to tropical cyclones cause great damage in coastal areas; therefore, accurately predicting storm surges and high waves before a typhoon strike is crucial. Meteorological forcing is an important factor for predicting these catastrophic events. This study presents an improved methodology for determining accurate meteorological forcing. Typhoon Chaba, which caused serious damage to the south coast of South Korea in 2016, was selected as a case study. In this study, symmetric and asymmetric parametric vortex models based on the typhoon track forecasted by the Model for Prediction Across Scales (MPAS) were used to create meteorological forcing and were compared with those models based on the best track. The meteorological fields were also created by blending the meteorological field from the symmetric / asymmetric parametric vortex models based on the MPAS-forecasted typhoon track and the meteorological field generated by the forecasting model (MPAS). This meteorological forcing data was then used given to two-way coupled tide-surge-wave models: Advanced CIRCulation (ADCIRC) and Simulating Waves Nearshore (SWAN). The modeled storm surges and waves correlated well with the observations and were comparable to those predicted using the best track. Based on our analysis, we propose using the parametric model with the MPAS-forecasted track, the meteorological field from the same forecasting model, and blending them to improve storm surge and wave prediction.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.446-449
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• 2001

본 논문에서는 태풍의 진로와 세기를 ES_BLRNN을 이용해 예측하였다. 기존의 방법인 수치모델이나. CLIPER모델을 사용함에 있어서, 통계적 방법인 CLIPER모델은 예측성능면에서 수치모델보다 그 성능이 떨어지고, 반면에 수치모델의 성능은 CLIPER 모델에 비해 우수하나 슈퍼컴퓨터(Cray-2S, FUSITSU)를 이용하여야만 예보가 가능한 제약점을 가지고 있다. 또한 수치모델을 슈퍼컴퓨터로 계산할 경우 약 30분 정도가 소요되는 점을 감안할 때, ES_BLRNN은 이들의 단점을 보안할 수 있는 하나의 방편이라 생각된다. 게다가 ES_BLRNN의 경우 개인용 컴퓨터로도 충분히 사용 가능할 만큼 비용이 저렴하고, 681개의 태풍을 학습할 때 결리는 시간은 약 5분 정도이며, 146개의 태풍을 예측하는데 걸리는 시간은 약 3초 정도(Pentium MMX 200 Processor, RAM 64m, OS: RedHat LINUX 5.2. language ; ANSI-C)로써, 슈퍼컴퓨터나 CLIPER모델에 비해 훨씬 빠르게 결과를 볼 수 있다.

• Atmosphere
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• v.24 no.4
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• pp.565-571
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• 2014

A long-range prediction system of tropical cyclone (TC) activity over the western North Pacific (WNP) has been operated in the National Typhoon Center of the Korea Meteorological Administration since 2012. The model forecasts the spatial distribution of TC tracks averaged over the period June~October. In this study, we separately developed TC prediction models for summer (June~August) and autumn (September~November) period based on the current operating system. To perform the three-month WNP TC activity prediction procedure readily, we modified the shell script calling in environmental variables automatically. The user can apply the model by changing these environmental variables of namelist parameter in consideration of their objective. The validations for the two seasons demonstrate the great performance of predictions showing high pattern correlations between hindcast and observed TC activity. In addition, we developed a post-processing script for deducing TC activity in the Korea emergency zone from final forecasting map and its skill is discussed.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.21 no.1
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• pp.79-90
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• 2009

This paper presents the development of dynamically combined Typhoon generated surge-tide-wave numerical model which is applicable from deep to shallow water. The dynamically coupled model consists of hydrodynamic module and wind wave module. The hydrodynamic module is modified from POM and wind wave module is modified from WAM to be applicable from deep to shallow water. Hydrodynamic module computes tidal currents, sea surface elevations and storm surges and provide these information to wind wave module. Wind wave mudule computes wind waves and provides computed information such as radiation stress, sea surface roughness and shear stress due to winds. The newly developed model was applied to compute the surge, tide and wave fields by typhoon Maemi. Verification of model performance was made by comparison of measured waves and tide data with simulated results.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1037-1051
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• 2020

The accurate monitoring and forecasting of the intensity of tropical cyclones (TCs) are able to effectively reduce the overall costs of disaster management. In this study, we proposed a multi-task learning (MTL) based deep learning model for real-time TC intensity estimation and forecasting with the lead time of 6-12 hours following the event, based on the fusion of geostationary satellite images and numerical forecast model output. A total of 142 TCs which developed in the Northwest Pacific from 2011 to 2016 were used in this study. The Communications system, the Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) data were used to extract the images of typhoons, and the Climate Forecast System version 2 (CFSv2) provided by the National Center of Environmental Prediction (NCEP) was employed to extract air and ocean forecasting data. This study suggested two schemes with different input variables to the MTL models. Scheme 1 used only satellite-based input data while scheme 2 used both satellite images and numerical forecast modeling. As a result of real-time TC intensity estimation, Both schemes exhibited similar performance. For TC intensity forecasting with the lead time of 6 and 12 hours, scheme 2 improved the performance by 13% and 16%, respectively, in terms of the root mean squared error (RMSE) when compared to scheme 1. Relative root mean squared errors(rRMSE) for most intensity levels were lessthan 30%. The lower mean absolute error (MAE) and RMSE were found for the lower intensity levels of TCs. In the test results of the typhoon HALONG in 2014, scheme 1 tended to overestimate the intensity by about 20 kts at the early development stage. Scheme 2 slightly reduced the error, resulting in an overestimation by about 5 kts. The MTL models reduced the computational cost about 300% when compared to the single-tasking model, which suggested the feasibility of the rapid production of TC intensity forecasts.

• 한국지구과학회:학술대회논문집
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• 2004.02a
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• pp.38-38
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• 2004

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.11a
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• pp.34-35
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• 2018

낙동강 하류역은 최근의 퇴적우세 지형변화와 더불어, 기후변화에 따른 태풍강도 강화 등으로 인한 해일고 증가가 우려된다. 따라서, 과거 태풍자료를 수집 분석한 후 연구지역에 가장 큰 영향을 미친 태풍을 모델 태풍으로 선정하여 낙동강 하류역에 위치한 주요지점별 폭풍해일고 변화를 파악하였다. 실험결과, 최대 폭풍해일고는 태풍 매미 내습시에 나타났으며, 하단 매립지 전면에서 1.1~1.5m, 명지주거단지 전면에서 1.2~1.3m, 녹산국가산업단지 전면에서 1.3~1.5m로 하단 매립지 전면이 가장 크게 나타났다. 향후, 과거 지형변화를 고려한 폭풍해일고 검토를 통하여 최근의 급격한 지형변화로 인한 영향을 파악한 대비를 해야 할 것으로 사료된다.

• Proceedings of KOSOMES biannual meeting
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• 2017.11a
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• pp.242-242
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• 2017