JongCheol Pyo

3.3k total citations
66 papers, 2.0k citations indexed

About

JongCheol Pyo is a scholar working on Water Science and Technology, Environmental Engineering and Industrial and Manufacturing Engineering. According to data from OpenAlex, JongCheol Pyo has authored 66 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Water Science and Technology, 27 papers in Environmental Engineering and 23 papers in Industrial and Manufacturing Engineering. Recurrent topics in JongCheol Pyo's work include Water Quality Monitoring and Analysis (22 papers), Marine and coastal ecosystems (19 papers) and Water Quality Monitoring Technologies (17 papers). JongCheol Pyo is often cited by papers focused on Water Quality Monitoring and Analysis (22 papers), Marine and coastal ecosystems (19 papers) and Water Quality Monitoring Technologies (17 papers). JongCheol Pyo collaborates with scholars based in South Korea, United States and Japan. JongCheol Pyo's co-authors include Kyung Hwa Cho, Sang‐Soo Baek, Yong Sung Kwon, Jong Ahn Chun, Yakov Pachepsky, Seok Min Hong, Moon S. Kim, Yongeun Park, Mayzonee Ligaray and Hyuk Lee and has published in prestigious journals such as The Astrophysical Journal, The Science of The Total Environment and Water Research.

In The Last Decade

JongCheol Pyo

64 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
JongCheol Pyo South Korea 24 942 770 457 363 266 66 2.0k
Qiao Wang China 22 421 0.4× 208 0.3× 303 0.7× 448 1.2× 373 1.4× 140 1.6k
Nguyễn Thị Thu Hà Vietnam 19 503 0.5× 235 0.3× 378 0.8× 557 1.5× 186 0.7× 89 1.6k
Kyunghyun Kim South Korea 20 508 0.5× 294 0.4× 216 0.5× 187 0.5× 146 0.5× 89 1.2k
Guangxin Zhang China 29 987 1.0× 410 0.5× 144 0.3× 184 0.5× 823 3.1× 195 2.7k
Jing Tang China 26 486 0.5× 239 0.3× 115 0.3× 535 1.5× 795 3.0× 97 2.6k
Ying Zhao China 25 329 0.3× 172 0.2× 289 0.6× 554 1.5× 286 1.1× 69 2.9k
Salah Elsayed Egypt 28 925 1.0× 947 1.2× 177 0.4× 54 0.1× 230 0.9× 99 2.7k
Jingjie Zhang China 27 442 0.5× 302 0.4× 117 0.3× 221 0.6× 233 0.9× 73 2.1k
Anthony Vodacek United States 21 190 0.2× 179 0.2× 325 0.7× 990 2.7× 509 1.9× 80 2.0k
Yun Du China 39 956 1.0× 1.1k 1.4× 133 0.3× 159 0.4× 1.5k 5.7× 211 5.1k

Countries citing papers authored by JongCheol Pyo

Since Specialization
Citations

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

Fields of papers citing papers by JongCheol Pyo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by JongCheol Pyo. 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 JongCheol Pyo. The network helps show where JongCheol Pyo may publish in the future.

Co-authorship network of co-authors of JongCheol Pyo

This figure shows the co-authorship network connecting the top 25 collaborators of JongCheol Pyo. A scholar is included among the top collaborators of JongCheol Pyo 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 JongCheol Pyo. JongCheol Pyo 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.
Pyo, JongCheol, et al.. (2025). Predicting radionuclide behavior in deep geological repositories using graph convolutional long short-term memory. Journal of Hazardous Materials. 496. 139195–139195.
2.
Pyo, JongCheol, et al.. (2025). Development of deep learning quantization framework for remote sensing edge device to estimate inland water quality in South Korea. Water Research. 283. 123760–123760. 3 indexed citations
3.
Kim, Soobin, Eunhee Lee, Hyoun‐Tae Hwang, et al.. (2024). Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models. Water Research X. 23. 100228–100228. 11 indexed citations
4.
5.
Jung, Sangjin, et al.. (2024). Improving fecal bacteria estimation using machine learning and explainable AI in four major rivers, South Korea. The Science of The Total Environment. 957. 177459–177459. 4 indexed citations
6.
Shin, Jihoon, TaeHo Kim, Kyung Hwa Cho, et al.. (2023). Deep learning-based efficient drone-borne sensing of cyanobacterial blooms using a clique-based feature extraction approach. The Science of The Total Environment. 912. 169540–169540. 16 indexed citations
7.
Hong, Seok Min, Ather Abbas, Soobin Kim, et al.. (2023). Autonomous calibration of EFDC for predicting chlorophyll-a using reinforcement learning and a real-time monitoring system. Environmental Modelling & Software. 168. 105805–105805. 23 indexed citations
8.
Pyo, JongCheol, Yakov Pachepsky, Soobin Kim, et al.. (2023). Long short-term memory models of water quality in inland water environments. Water Research X. 21. 100207–100207. 39 indexed citations
9.
Baek, Sang‐Soo, Eun‐Young Jung, JongCheol Pyo, et al.. (2022). Hierarchical deep learning model to simulate phytoplankton at phylum/class and genus levels and zooplankton at the genus level. Water Research. 218. 118494–118494. 21 indexed citations
10.
Jang, Jiyi, et al.. (2022). A novel method for micropollutant quantification using deep learning and multi-objective optimization. Water Research. 212. 118080–118080. 12 indexed citations
11.
Park, Yongeun, JongCheol Pyo, Sanghyun Park, et al.. (2022). Optimal Band Selection for Airborne Hyperspectral Imagery to Retrieve a Wide Range of Cyanobacterial Pigment Concentration Using a Data-Driven Approach. Remote Sensing. 14(7). 1754–1754. 9 indexed citations
12.
Baek, Sang‐Soo, Yong Sung Kwon, JongCheol Pyo, et al.. (2021). Identification of influencing factors of A. catenella bloom using machine learning and numerical simulation. Harmful Algae. 103. 102007–102007. 13 indexed citations
13.
Baek, Sang‐Soo, et al.. (2021). Analysis of micropollutants in a marine outfall using network analysis and decision tree. The Science of The Total Environment. 806(Pt 4). 150938–150938. 14 indexed citations
14.
Baek, Sang‐Soo, JongCheol Pyo, Yong Sung Kwon, et al.. (2021). Deep Learning for Simulating Harmful Algal Blooms Using Ocean Numerical Model. Frontiers in Marine Science. 8. 20 indexed citations
15.
Pyo, JongCheol, Yong Sung Kwon, Joong‐Hyuk Min, et al.. (2021). Effect of hyperspectral image-based initial conditions on improving short-term algal simulation of hydrodynamic and water quality models. Journal of Environmental Management. 294. 112988–112988. 18 indexed citations
16.
Pyo, JongCheol, et al.. (2021). Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage. Water Research. 203. 117483–117483. 58 indexed citations
17.
Pyo, JongCheol, et al.. (2020). Using convolutional neural network for predicting cyanobacteria concentrations in river water. Water Research. 186. 116349–116349. 91 indexed citations
18.
Pyo, JongCheol, Yakov Pachepsky, Hyuk Lee, et al.. (2016). Chlorophyll- a concentration estimation using three difference bio-optical algorithms, including a correction for the low-concentration range: the case of the Yiam reservoir, Korea. Remote Sensing Letters. 7(5). 407–416. 14 indexed citations
19.
Cho, Kyung Hwa, Yakov Pachepsky, Minjeong Kim, et al.. (2016). Modeling seasonal variability of fecal coliform in natural surface waters using the modified SWAT. Journal of Hydrology. 535. 377–385. 48 indexed citations
20.
Pyo, JongCheol, S. S. Hong, M. Ishiguro, et al.. (2009). The Zodiacal Dust Cloud Revealed by the AKARI IRC All-Sky Survey. ASPC. 418. 39. 1 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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