Tae‐Soo Chon

3.3k total citations
116 papers, 2.5k citations indexed

About

Tae‐Soo Chon is a scholar working on Nature and Landscape Conservation, Ecology and Artificial Intelligence. According to data from OpenAlex, Tae‐Soo Chon has authored 116 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Nature and Landscape Conservation, 38 papers in Ecology and 21 papers in Artificial Intelligence. Recurrent topics in Tae‐Soo Chon's work include Fish Ecology and Management Studies (35 papers), Neural Networks and Applications (21 papers) and Freshwater macroinvertebrate diversity and ecology (19 papers). Tae‐Soo Chon is often cited by papers focused on Fish Ecology and Management Studies (35 papers), Neural Networks and Applications (21 papers) and Freshwater macroinvertebrate diversity and ecology (19 papers). Tae‐Soo Chon collaborates with scholars based in South Korea, China and United States. Tae‐Soo Chon's co-authors include Young‐Seuk Park, Ihn–Sil Kwak, Mi‐Young Song, Chunlei Xia, Yuedan Liu, Zongming Ren, Sovan Lek, Mi‐Jung Bae, Jang-Myung Lee and Sang‐Hee Lee 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

Tae‐Soo Chon

111 papers receiving 2.4k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Tae‐Soo Chon South Korea 27 1.0k 827 487 299 270 116 2.5k
Young‐Seuk Park South Korea 38 2.2k 2.1× 1.6k 2.0× 843 1.7× 597 2.0× 258 1.0× 201 4.7k
Michele Scardi Italy 29 676 0.7× 489 0.6× 297 0.6× 148 0.5× 92 0.3× 86 2.3k
Li Wen Australia 27 1.2k 1.2× 489 0.6× 639 1.3× 204 0.7× 75 0.3× 144 2.3k
Ernst Linder United States 24 977 1.0× 274 0.3× 245 0.5× 287 1.0× 221 0.8× 47 2.8k
Lindi J. Quackenbush United States 30 1.9k 1.9× 619 0.7× 307 0.6× 2.1k 6.9× 235 0.9× 75 4.0k
Yue Shi China 26 1.4k 1.3× 377 0.5× 89 0.2× 424 1.4× 1.2k 4.3× 70 3.0k
Marcos Gervásio Pereira Brazil 34 1.3k 1.3× 488 0.6× 532 1.1× 314 1.1× 1.8k 6.6× 598 6.2k
Lifen Jiang China 30 1.9k 1.9× 614 0.7× 102 0.2× 131 0.4× 936 3.5× 94 4.0k
Thomas Udelhoven Germany 32 1.5k 1.5× 181 0.2× 195 0.4× 1.2k 4.1× 503 1.9× 95 3.4k
Aditya Singh United States 31 1.6k 1.6× 428 0.5× 123 0.3× 573 1.9× 1.1k 4.1× 141 3.3k

Countries citing papers authored by Tae‐Soo Chon

Since Specialization
Citations

This map shows the geographic impact of Tae‐Soo Chon'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 Tae‐Soo Chon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tae‐Soo Chon more than expected).

Fields of papers citing papers by Tae‐Soo Chon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tae‐Soo Chon. 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 Tae‐Soo Chon. The network helps show where Tae‐Soo Chon may publish in the future.

Co-authorship network of co-authors of Tae‐Soo Chon

This figure shows the co-authorship network connecting the top 25 collaborators of Tae‐Soo Chon. A scholar is included among the top collaborators of Tae‐Soo Chon 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 Tae‐Soo Chon. Tae‐Soo Chon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Park, Yung Chul, et al.. (2022). Network Analysis Using Markov Chain Applied to Wildlife Habitat Selection. Diversity. 14(5). 330–330. 2 indexed citations
3.
Xia, Chunlei, Tae‐Soo Chon, Fugo Takasu, Won Il Choi, & Young‐Seuk Park. (2022). Simulating Pine Wilt Disease Dispersal With an Individual-Based Model Incorporating Individual Movement Patterns of Vector Beetles. Frontiers in Plant Science. 13. 886867–886867. 3 indexed citations
4.
Hong, Sungwon, Tae‐Soo Chon, & Gea‐Jae Joo. (2020). Spatial Distribution Patterns of Eurasian Otter (Lutra Lutra) in Association with Environmental Factors Unravelled by Machine Learning and Diffusion Kernel Method. Journal of Environmental Informatics. 11 indexed citations
5.
Kim, Hyo Gyeom, Sungwon Hong, Tae‐Soo Chon, & Gea‐Jae Joo. (2020). Spatial patterning of chlorophyll a and water-quality measurements for determining environmental thresholds for local eutrophication in the Nakdong River basin. Environmental Pollution. 268(Pt A). 115701–115701. 41 indexed citations
6.
Nguyen, Thao Thi, et al.. (2017). Comparative and bioinformatics analyses of pathogenic bacterial secretomes identified by mass spectrometry in Burkholderia species. The Journal of Microbiology. 55(7). 568–582. 3 indexed citations
7.
Ren, Zongming, Tingting Zhang, Na Xing, et al.. (2016). Behavior persistence in defining threshold switch in stepwise response of aquatic organisms exposed to toxic chemicals. Chemosphere. 165. 409–417. 13 indexed citations
8.
Xia, Chunlei, et al.. (2013). Plant leaf detection using modified active shape models. Biosystems Engineering. 116(1). 23–35. 37 indexed citations
9.
Jeong, Kwang‐Seuk, et al.. (2012). Machine Learning for Predictive Management: Short and Long term Prediction of Phytoplankton Biomass using Genetic Algorithm Based Recurrent Neural Networks. International Journal of Environmental Research. 6(1). 95–108. 19 indexed citations
10.
Zhang, Gaosheng, Linlin Chen, Jing Chen, et al.. (2012). Evidence for the Stepwise Behavioral Response Model (SBRM): The effects of Carbamate Pesticides on medaka (Oryzias latipes) in an online monitoring system. Chemosphere. 87(7). 734–741. 26 indexed citations
11.
Tuyen, Nguyen Van, et al.. (2011). Mathematical models applied to dispersal data of pest populations in greenhouse. 157–157.
12.
Lee, Sang‐Hee & Tae‐Soo Chon. (2011). Effects of Climate Change on Subterranean Termite Territory Size: A Simulation Study. Journal of Insect Science. 11(80). 1–14. 6 indexed citations
13.
Do, Younghae, et al.. (2007). Detecting response behaviors of Medaka after the treatments of heavy metal in the boundary areas. 3(2). 39–39. 2 indexed citations
14.
Pak, Hyuk Kyu, et al.. (2005). Dynamics of prey-flock escaping behavior in response to predator's attack. Journal of Theoretical Biology. 240(2). 250–259. 56 indexed citations
15.
Park, Young‐Seuk, et al.. (2005). Computational characterization of behavioral response of medaka (Oryzias latipes) treated with diazinon. Aquatic Toxicology. 71(3). 215–228. 57 indexed citations
16.
Song, Mi‐Young, et al.. (2002). Benthic-Macro Invertebrates in Streams of South Korea in Different Levels of Pollution and Patterning of Communities by Implementing the Self-Organizing Mapping. 156–156. 2 indexed citations
17.
18.
Chon, Tae‐Soo, Ihn–Sil Kwak, & Young‐Seuk Park. (2000). Pattern Recognition of Long-term Ecological Data in Community Changes by Using Artificial Neural Networks: Benthic Macroinvertebrates and Chironomids in a Polluted Stream. The Korean Journal of Ecology. 23(2). 89–100. 1 indexed citations
19.
Chon, Tae‐Soo, et al.. (1995). Monthly Changes in Benthic Macroinvertebrate Communities in Different Saprobities in the Suyong and Soktae Streams of the Suyong River. The Korean Journal of Ecology. 18(1). 157–177. 4 indexed citations
20.
Chon, Tae‐Soo, et al.. (1991). A Study on the Benthic Macroinvertebrates in the Middle Reaches of Paenae Stream , a Tributary of the Naktong River , Korea 2 . Comparison of Communities and Environments at the Upper and Lower Sites of Levees. The Korean Journal of Ecology. 14(4). 399–413. 3 indexed citations

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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