YoonKyung Cha

1.9k total citations
62 papers, 1.5k citations indexed

About

YoonKyung Cha is a scholar working on Water Science and Technology, Environmental Chemistry and Nature and Landscape Conservation. According to data from OpenAlex, YoonKyung Cha has authored 62 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Water Science and Technology, 27 papers in Environmental Chemistry and 19 papers in Nature and Landscape Conservation. Recurrent topics in YoonKyung Cha's work include Fish Ecology and Management Studies (19 papers), Soil and Water Nutrient Dynamics (18 papers) and Hydrological Forecasting Using AI (15 papers). YoonKyung Cha is often cited by papers focused on Fish Ecology and Management Studies (19 papers), Soil and Water Nutrient Dynamics (18 papers) and Hydrological Forecasting Using AI (15 papers). YoonKyung Cha collaborates with scholars based in South Korea, United States and Lebanon. YoonKyung Cha's co-authors include Craig A. Stow, Jihoon Shin, Kyung Hwa Cho, Hyuk Lee, TaeHo Kim, Young Woo Kim, Taegu Kang, Kenneth H. Reckhow, Laura T. Johnson and Remegio Confesor and has published in prestigious journals such as SHILAP Revista de lepidopterología, Environmental Science & Technology and The Science of The Total Environment.

In The Last Decade

YoonKyung Cha

57 papers receiving 1.4k citations

Peers

Countries citing papers authored by YoonKyung Cha

Since Specialization

Citations

This map shows the geographic impact of YoonKyung Cha's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by YoonKyung Cha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites YoonKyung Cha more than expected).

Fields of papers citing papers by YoonKyung Cha

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by YoonKyung Cha. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by YoonKyung Cha. The network helps show where YoonKyung Cha may publish in the future.

Co-authorship network of co-authors of YoonKyung Cha

This figure shows the co-authorship network connecting the top 25 collaborators of YoonKyung Cha. A scholar is included among the top collaborators of YoonKyung Cha based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with YoonKyung Cha. YoonKyung Cha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Spatiotemporal dynamics of summer chlorophyll-a concentrations under varying drought conditions in a hierarchical Bayesian model 2025 ·Chemical Engineering Journal ·(unknown), YoonKyung Cha, (unknown), Hyun‐Han Kwon	0
2	Generalizable deep learning forecasting of harmful algal blooms using transfer learning across river systems 2025 ·Ecological Informatics ·(unknown), (unknown), (unknown), Jihoon Shin, Kyunghyun Kim, Jin‐Woo Jung, Hongtae Kim, YoonKyung Cha	0
3	A modular deep learning surrogate model for simulating harmful algal blooms in complex process-based systems 2025 ·Water Research ·Young Woo Kim, YoonKyung Cha, Jihoon Shin	1
4	Modeling ecosystem-wide responses to environmental stressors: A multi-trophic hierarchical Bayesian network approach 2025 ·Journal of Environmental Management ·(unknown), (unknown), (unknown), Jason J. Jung, (unknown), (unknown), Hyun‐Han Kwon, YoonKyung Cha	0
5	Development of a deep learning–based feature stream network for forecasting riverine harmful algal blooms from a network perspective 2024 ·Water Research ·Jihoon Shin, YoonKyung Cha	9
6	Deep learning-based efficient drone-borne sensing of cyanobacterial blooms using a clique-based feature extraction approach 2023 ·The Science of The Total Environment ·Jihoon Shin, (unknown), TaeHo Kim, Kyung Hwa Cho, Seok Min Hong, (unknown), JongCheol Pyo, YoonKyung Cha	16
7	Hybrid model for daily streamflow and phosphorus load prediction 2023 ·Water Science & Technology ·(unknown), Jihoon Shin, TaeHo Kim, (unknown), (unknown), (unknown), YoonKyung Cha	3
8	Expansion of Roadside Air Pollution Monitoring Network in Seoul using Learning-based Optimization Method 2023 ·SHILAP Revista de lepidopterología ·TaeHo Kim, Jihoon Shin, Young Woo Kim, (unknown), (unknown), (unknown), YoonKyung Cha	0
9	Incorporation of feature engineering and attention mechanisms into deep learning models to develop an early warning system for harmful algal blooms 2023 ·Journal of Cleaner Production ·TaeHo Kim, Jihoon Shin, YoonKyung Cha	6
10	Data-driven models for predicting community changes in freshwater ecosystems: A review 2023 ·Ecological Informatics ·(unknown), (unknown), Dae‐Seong Lee, YoonKyung Cha, Joong‐Hyuk Min, Young‐Seuk Park	19
11	Projected climate change impact on cyanobacterial bloom phenology in temperate rivers based on temperature dependency 2023 ·Water Research ·Hyo Gyeom Kim, YoonKyung Cha, Kyung Hwa Cho	26
12	Machine learning-guided prediction of potential engineering targets for microbial production of lycopene 2022 ·Bioresource Technology ·(unknown), Jihoon Shin, YoonKyung Cha, Min Sun Kim, Min Sun Choi, TaeHo Kim, Young-Kwon Park, Yong Jun Choi	13
13	Simultaneous feature engineering and interpretation: Forecasting harmful algal blooms using a deep learning approach 2022 ·Water Research ·TaeHo Kim, Jihoon Shin, Doyeon Lee, Young Woo Kim, Eun Hye Na, (unknown), (unknown), YoonKyung Cha	45
14	An interpretable machine learning method for supporting ecosystem management: Application to species distribution models of freshwater macroinvertebrates 2021 ·Journal of Environmental Management ·YoonKyung Cha, Jihoon Shin, (unknown), Dae‐Seong Lee, Young Woo Kim, TaeHo Kim, Young‐Seuk Park	79
15	Learning hierarchical Bayesian networks to assess the interaction effects of controlling factors on spatiotemporal patterns of fecal pollution in streams 2021 ·The Science of The Total Environment ·TaeHo Kim, Doyeon Lee, Jihoon Shin, Young Woo Kim, YoonKyung Cha	5
16	The implications of Simpson's paradox for cross-scale inference among lakes 2019 ·Water Research ·Song S. Qian, Craig A. Stow, (unknown), Jemma Stachelek, YoonKyung Cha, Ibrahim Alameddine, Patricia A. Soranno	15
17	The Effects of Tree Species on Soil Organic Carbon Content in South Korea 2019 ·Journal of Geophysical Research Biogeosciences ·(unknown), YoonKyung Cha, Neung‐Hwan Oh	29
18	The relative importance of water temperature and residence time in predicting cyanobacteria abundance in regulated rivers 2017 ·Water Research ·YoonKyung Cha, Kyung Hwa Cho, Hyuk Lee, Taegu Kang, Joon Ha Kim	121
19	Bayesian modeling approach for characterizing groundwater arsenic contamination in the Mekong River basin 2015 ·Chemosphere ·YoonKyung Cha, Young Mo Kim, Jae‐Woo Choi, Suthipong Sthiannopkao, Kyung Hwa Cho	15
20	Phosphorus load estimation in the Saginaw River, MI using a Bayesian hierarchical/multilevel model 2010 ·Water Research ·YoonKyung Cha, Craig A. Stow, Kenneth H. Reckhow, (unknown), Thomas H. Johengen	24

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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