Jungsu Park

2.1k total citations · 1 hit paper
63 papers, 1.5k citations indexed

About

Jungsu Park is a scholar working on Water Science and Technology, Environmental Engineering and Industrial and Manufacturing Engineering. According to data from OpenAlex, Jungsu Park has authored 63 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, 17 papers in Environmental Engineering and 11 papers in Industrial and Manufacturing Engineering. Recurrent topics in Jungsu Park's work include Hydrological Forecasting Using AI (14 papers), Water Quality Monitoring Technologies (14 papers) and Anaerobic Digestion and Biogas Production (9 papers). Jungsu Park is often cited by papers focused on Hydrological Forecasting Using AI (14 papers), Water Quality Monitoring Technologies (14 papers) and Anaerobic Digestion and Biogas Production (9 papers). Jungsu Park collaborates with scholars based in South Korea, United States and India. Jungsu Park's co-authors include Woo Hyoung Lee, Keugtae Kim, Sang–Hyoun Kim, Tae‐Young Heo, Cheol Young Park, Younggyu Son, Gi Beom Kim, Roent Dune A. Cayetano, Sang‐hun Lee and Yung‐Hun Yang and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Science of The Total Environment and Bioresource Technology.

In The Last Decade

Jungsu Park

58 papers receiving 1.5k citations

Hit Papers

Interpretation of ensemble learning to predict water qual... 2022 2026 2023 2024 2022 50 100 150
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Interpretation of ensemble learning to predict water quality using explainable artificial intelligence
    2022 160 citations Jungsu Park, Woo Hyoung Lee et al. The Science of The Total Environment profile →

Peers

Jungsu Park
Borja Valverde‐Pérez Denmark
Tao Lyu China
Jinhui Jeanne Huang‬‬‬‬ China
Zhongwei Wang China
Hongda Fang China
Belinda Sturm United States
Runze Xu China
Enrique Nebot Spain
Dongsheng Wang China
Keugtae Kim South Korea
Borja Valverde‐Pérez Denmark
Jungsu Park
Citations per year, relative to Jungsu Park Jungsu Park (= 1×) peers Borja Valverde‐Pérez

Countries citing papers authored by Jungsu Park

Since Specialization
Citations

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

Fields of papers citing papers by Jungsu Park

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jungsu Park

This figure shows the co-authorship network connecting the top 25 collaborators of Jungsu Park. A scholar is included among the top collaborators of Jungsu Park 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 Jungsu Park. Jungsu Park 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.
Park, Jungsu, et al.. (2025). Advancements in artificial intelligence-based technologies for PFAS detection, monitoring, and management. The Science of The Total Environment. 980. 179536–179536. 14 indexed citations
2.
Kim, Gi Beom, et al.. (2025). Continuous semi-dry anaerobic co-digestion of cattle manure and food waste: Performance evaluation and microbial community dynamics. Environmental Technology & Innovation. 40. 104676–104676.
3.
Kim, Gi Beom, et al.. (2024). Effects of solids concentration and thermal pretreatment on continuous digestion of undigested dewatered sludge. Renewable Energy. 231. 120894–120894. 2 indexed citations
4.
Kim, Doyun, et al.. (2024). Real-time chlorophyll-a forecasting using machine learning framework with dimension reduction and hyperspectral data. Environmental Research. 262(Pt 1). 119823–119823. 11 indexed citations
5.
Park, Jungsu, et al.. (2024). Recent advances in algal bloom detection and prediction technology using machine learning. The Science of The Total Environment. 938. 173546–173546. 31 indexed citations
6.
Munshi, Faris M., Jae-Hoon Hwang, Jungsu Park, et al.. (2023). Electric field forward osmosis (EFFO) fouling mitigation in algae harvesting. Separation and Purification Technology. 327. 124868–124868. 9 indexed citations
7.
Park, Jungsu, et al.. (2023). The use of explainable artificial intelligence for interpreting the effect of flow phase and hysteresis on turbidity prediction. Environmental Earth Sciences. 82(15). 6 indexed citations
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Morya, Raj, Madan Kumar, Isha Tyagi, et al.. (2022). Recent advances in black liquor valorization. Bioresource Technology. 350. 126916–126916. 73 indexed citations
11.
Jia, Ru, et al.. (2022). Exploration of deep learning models for real-time monitoring of state and performance of anaerobic digestion with online sensors. Bioresource Technology. 363. 127908–127908. 26 indexed citations
12.
Raj, Tirath, K. Chandrasekhar, Jungsu Park, et al.. (2022). Synthesis of fatty acid-based ammonium ionic liquids and their application for extraction of Co(II) and Ni(II) metals ions from aqueous solution. Chemosphere. 307(Pt 2). 135787–135787. 13 indexed citations
13.
Pandey, Ashutosh Kumar, et al.. (2022). Machine learning in fermentative biohydrogen production: Advantages, challenges, and applications. Bioresource Technology. 370. 128502–128502. 46 indexed citations
14.
Cayetano, Roent Dune A., et al.. (2022). The effects of ammonia acclimation on biogas recovery and the microbial population in continuous anaerobic digestion of swine manure. Environmental Research. 212(Pt D). 113483–113483. 29 indexed citations
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Park, Jungsu, et al.. (2020). A review on recent advances in water and wastewater treatment facilities management for earthquake disaster response. Journal of The Korean Society of Water and Wastewater. 34(1). 9–21. 1 indexed citations
17.
Park, Jungsu, Younggyu Son, & Woo Hyoung Lee. (2019). Variation of efficiencies and limits of ultrasonication for practical algal bloom control in fields. Ultrasonics Sonochemistry. 55. 8–17. 34 indexed citations
18.
Park, Jungsu & James Hunt. (2018). Modeling fine particle dynamics in gravel-bedded streams: Storage and re-suspension of fine particles. The Science of The Total Environment. 634. 1042–1053. 18 indexed citations
19.
Kweon, Jihyang, et al.. (2017). Effects of sodium hydroxide cleaning on polyvinylidene fluoride fouled with humic water. Membrane Water Treatment. 8(2). 149–160. 3 indexed citations
20.
Kim, Young‐Hoo, Yong‐Keun Choi, Jungsu Park, et al.. (2011). Ionic liquid-mediated extraction of lipids from algal biomass. Bioresource Technology. 109. 312–315. 162 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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