Chang-Hoo Jeong

Mr. Chang-Hoo Jeong


Research Focus & Metrics

Academic Career Length

18Years (2009-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
11 January 2024 in Computers & Geosciences
Verified
Journal Article
Open access
WKWonsu Kim
Wonsu Kim
CJChang-Hoo Jeong
Chang-Hoo Jeong
SKSeongchan Kim
Seongchan Kim

23 July 2022 in The Journal of Supercomputing
Verified
Journal Article
Closed access
PDF file
CJChang-Hoo Jeong
Chang-Hoo Jeong
Mun Yong YiMun Yong Yi
Mun Yong Yi

13 April 2020 in Water
Verified
Journal Article
Open access
PDF file
DJDongmin Jang
Dongmin Jang
WJWon-Kyun Joo
Won-Kyun Joo
CJChang-Hoo Jeong
Chang-Hoo Jeong
WKWonsu Kim
Wonsu Kim
SPSung Won Park
Sung Won Park
YSYoojin Song
Yoojin Song
Typhoons can often cause inundation in lower coastal cities by inducing strong surges and waves. Being affected by typhoon annually, the coastal cities in South Korea are very vulnerable to typhoons. In 2016, a typhoon ‘CHABA’, with a maximum 10 min sustained wind speed of about 50 m/s and a minimum central pressure of 905 hPa, hit South Korea, suffering tremendous damage. In particular, ‘CHABA’-induced coastal inundation resulted in serious damage to the coastal area of Busan where a lot of high-rise buildings and residential areas are concentrated, and was caused by the combined effect of tide, surge, and wave. The typhoon-induced surge raised sea levels during high tide, and the strong wave with a long period of more than 10 s eventually led to the coastal inundation at the same time. The present research focuses a numerical downscaling considering the effects of tide, surge and wave for coastal inundation induced by Typhoon ‘CHABA’. This downscaling approach applied several numerical models, which are the Weather Research and Forecasting model (WRF) for typhoon simulation, the Finite Volume Community Ocean Model (FVCOM) for tide and surge simulation, and the Simulating WAve Nearshore (SWAN) for wave simulation. In a domain covering the Korean Peninsula, typhoon-induced surges and waves were simulated applying the results simulated by WRF as meteorological conditions. In the downscaled domain ranged near the coastal area of Busan, the coastal inundation was simulated blending a storm tide height and an irregular wave height obtained from the domain, in which each height has 1 s interval. The irregular wave height was calculated using the significant wave height and peak period. Through this downscaling study, the impact of storm tide and wave on coastal inundation was estimated.

16 February 2016 in The Journal of Supercomputing
Verified
Journal Article
Closed access
PDF file
JUJung-Ho Um
Jung-Ho Um
Seungwoo LeeSeungwoo Lee
Seungwoo Lee
TKTaehong Kim
Taehong Kim
CJChang-Hoo Jeong
Chang-Hoo Jeong
SSSa-Kwang Song
Sa-Kwang Song
HJHamin Jung
Hamin Jung

30 September 2015 in Journal of the Korean Data and Information Science Society
Verified
Journal Article
Open access
Young-Min KimYoung-Min Kim
Young-Min Kim
MHMi-Nyeong Hwang
Mi-Nyeong Hwang
TKTaehong Kim
Taehong Kim
CJChang-Hoo Jeong
Chang-Hoo Jeong
DJDo-Heon Jeong
Do-Heon Jeong
???? ???? ??? ?? ?? ??? ??? ? ??? ???? ???? ???? ??? ?? ????? ? ??? ??, ??, ??, ???? ??? ? ??? ?? ????. ?? ?? ??? ??? ?? ???? ??? ?? ??? ???? ??, ?? ??? ??? ? ?? ?? ??? ?? ??? ?? ???? ??? ????. ? ??? ?? ?????, ???? ???? ??? ??? ?? ????? ??? ?? ???? ?? ????. ?? ?? ?? ?? ???? ???? ?? ??? ??? ? ?? ?? ???? ??? ? ?? ???? ??? ?? ??????? ??? ???? ? ???. ?? 2??? ????, ??? ???, ?? ??? ?? ?? ??? ???? 3??? ??? ??? ? ?? ?? ??? ?? ?? ??? ??? ????. 4???? ???? ??? ??? ???? ?? ???? ?? ???? ???? ???? ?????. Big data analysis for disaster have been recently started especially to text data such as social media. Social data usually supports for the final two stages of disaster management, which consists of four stages: prevention, preparation, response and recovery. Otherwise, big data analysis for meteorologic data can contribute to the prevention and preparation. This motivated us to review big data technologies dealing with non-text data rather than text in natural disaster area. To this end, we first explain the main keywords, big data, data mining and machine learning in sec. 2. Then we introduce the state-of-the-art machine learning techniques in meteorology-related field sec. 3. We show how the traditional machine learning techniques have been adapted for climatic data by taking into account the domain specificity. The application of these techniques in natural disaster response are then introduced (sec. 4), and we finally conclude with several future research directions.? ??? ??????? ? ??????????? SW???????????????? ???? ????? (B010-15-0353, ???? ?? ???? ? ??? ?? ??? ?????? ??? ??)

1 January 2015
Verified
Book Chapter
Closed access
PDF file
JUJung-Ho Um
Jung-Ho Um
Seungwoo LeeSeungwoo Lee
Seungwoo Lee
TKTaehong Kim
Taehong Kim
CJChang-Hoo Jeong
Chang-Hoo Jeong
KSKwangik Seo
Kwangik Seo
HJHamin Jung
Hamin Jung