K
Kyung Hwa Cho
Researcher at Ulsan National Institute of Science and Technology
Publications - 186
Citations - 5619
Kyung Hwa Cho is an academic researcher from Ulsan National Institute of Science and Technology. The author has contributed to research in topics: Chemistry & Water quality. The author has an hindex of 30, co-authored 157 publications receiving 3722 citations. Previous affiliations of Kyung Hwa Cho include University of Michigan & University of Massachusetts Amherst.
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Journal ArticleDOI
Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions
Anna M. Michalak,Eric J. Anderson,Dimitry Beletsky,Steven Boland,Nathan S. Bosch,Thomas B. Bridgeman,Justin D. Chaffin,Kyung Hwa Cho,Rem Confesor,Irem Daloğlu,Jospeh DePinto,Mary Anne Evans,Gary L. Fahnenstiel,Lingli He,Jeff C. Ho,Liza K. Jenkins,Liza K. Jenkins,Thomas H. Johengen,Kevin C Kuo,Elizabeth LaPorte,Xiaojian Liu,Michael McWilliams,Michael R. Moore,Derek J. Posselt,R. Peter Richards,Donald Scavia,Allison L. Steiner,Edward M. Verhamme,David M. Wright,Melissa A. Zagorski +29 more
TL;DR: It is shown that long-term trends in agricultural practices are consistent with increasing phosphorus loading to the western basin of the lake, and that these trends, coupled with meteorological conditions in spring 2011, produced record-breaking nutrient loads.
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Optimizing low impact development (LID) for stormwater runoff treatment in urban area, Korea: Experimental and modeling approach.
TL;DR: A novel methodology to optimize the sizes of different types of LID by conducting intensive stormwater monitoring and numerical modeling in a commercial site in Korea is proposed, which optimizes LID size in an attempt to moderate FFE on a receiving waterbody.
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Prediction of effluent concentration in a wastewater treatment plant using machine learning models
Hong Guo,Kwanho Jeong,Jiyeon Lim,Jeongwon Jo,Young Mo Kim,Jong-Pyo Park,Joon Ha Kim,Kyung Hwa Cho +7 more
TL;DR: The sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T- N concentration and modeling input values to integrated food waste and waste water treatment.
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Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea
TL;DR: An effective early-warning prediction method for Chl-a concentration and the eutrophication management scheme for reservoirs is suggested and a 7-day interval was determined as an efficient early warning interval in the two reservoirs.
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Evaluating Causes of Trends in Long-Term Dissolved Reactive Phosphorus Loads to Lake Erie
TL;DR: It is demonstrated that recent increased storm events, interacting with changes in fertilizer application timing and rate, as well as management practices that increase soil stratification and phosphorus accumulation at the soil surface, appear to drive the increasing DRP trend after the mid-1990s.