Sungwon Kim

5.0k total citations
121 papers, 3.4k citations indexed

About

Sungwon Kim is a scholar working on Environmental Engineering, Water Science and Technology and Artificial Intelligence. According to data from OpenAlex, Sungwon Kim has authored 121 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 84 papers in Environmental Engineering, 44 papers in Water Science and Technology and 41 papers in Artificial Intelligence. Recurrent topics in Sungwon Kim's work include Hydrological Forecasting Using AI (77 papers), Energy Load and Power Forecasting (28 papers) and Hydrology and Watershed Management Studies (26 papers). Sungwon Kim is often cited by papers focused on Hydrological Forecasting Using AI (77 papers), Energy Load and Power Forecasting (28 papers) and Hydrology and Watershed Management Studies (26 papers). Sungwon Kim collaborates with scholars based in South Korea, Iran and Algeria. Sungwon Kim's co-authors include Özgür Kişi, Vijay P. Singh, Youngmin Seo, Jalal Shiri, Meysam Alizamir, Hung Soo Kim, Mohammad Zounemat‐Kermani, Salim Heddam, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬ and Mohammad Ali Ghorbani and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Journal of Hazardous Materials.

In The Last Decade

Sungwon Kim

112 papers receiving 3.3k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sungwon Kim South Korea	34	2.0k	1.3k	1.2k	801	622	121	3.4k
Salim Heddam Algeria	39	2.5k 1.3×	2.1k 1.7×	1.1k 1.0×	821 1.0×	534 0.9×	175	4.4k
Mohammad Ali Ghorbani Iran	35	1.8k 0.9×	1.0k 0.8×	1.0k 0.9×	470 0.6×	448 0.7×	85	3.0k
H. Kerem Ciğizoğlu Türkiye	29	2.2k 1.1×	1.7k 1.3×	977 0.8×	400 0.5×	509 0.8×	56	3.2k
Yuk Feng Huang Malaysia	35	1.8k 0.9×	1.4k 1.1×	1.9k 1.7×	487 0.6×	443 0.7×	152	3.8k
Haitham Abdulmohsin Afan Malaysia	32	2.1k 1.0×	1.9k 1.5×	869 0.8×	404 0.5×	416 0.7×	71	3.5k
Mohammad Zounemat‐Kermani Iran	42	3.1k 1.5×	2.3k 1.8×	1.8k 1.5×	1.3k 1.6×	900 1.4×	154	5.7k
Isa Ebtehaj Iran	43	2.0k 1.0×	1.4k 1.1×	991 0.9×	592 0.7×	541 0.9×	129	4.4k
Anurag Malik India	40	2.0k 1.0×	1.5k 1.2×	1.9k 1.6×	480 0.6×	436 0.7×	122	3.8k
Babak Mohammadi Sweden	32	1.5k 0.8×	1.0k 0.8×	1.3k 1.1×	521 0.7×	458 0.7×	85	2.9k
Mohammad Ehteram Iran	34	1.7k 0.8×	1.7k 1.3×	803 0.7×	504 0.6×	514 0.8×	111	3.5k

Countries citing papers authored by Sungwon Kim

Since Specialization

Citations

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

Fields of papers citing papers by Sungwon Kim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sungwon Kim

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Nunno, Fabio Di, Francesco Granata, Sungwon Kim, et al.. (2025). Predicting water quality variables using gradient boosting machine: global versus local explainability using SHapley Additive Explanations (SHAP). Earth Science Informatics. 18(3). 13 indexed citations

Alizamir, Meysam, Kaywan Othman Ahmed, Salim Heddam, Sungwon Kim, & Jeong Eun Lee. (2025). Daily soil temperature prediction using hybrid deep learning and SHAP for sustainable soil management. Engineering Applications of Computational Fluid Mechanics. 19(1).

Alizamir, Meysam, Salim Heddam, & Sungwon Kim. (2025). A robust and explainable deep learning model based on an LSTM-CNN framework for reliable FDOM prediction in water quality monitoring: Incorporating SHAP analysis for enhanced interpretability. Process Safety and Environmental Protection. 201. 107594–107594. 2 indexed citations

Alizamir, Meysam, et al.. (2024). An efficient data fusion model based on Bayesian model averaging for robust water quality prediction using deep learning strategies. Expert Systems with Applications. 261. 125499–125499. 10 indexed citations

Kim, Sungwon, et al.. (2024). Decadal Climate and Landform Variables Analysis in Iraq Using Remote Sensing Datasets. SHILAP Revista de lepidopterología. 1(2). 2 indexed citations

Singh, Vijay Kumar, Dinesh Kumar Vishwakarma, Mohammad Ali Ghorbani, et al.. (2024). A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration. Scientific Reports. 14(1). 10638–10638. 18 indexed citations

Heddam, Salim, Sungwon Kim, Mariusz Ptak, et al.. (2023). River water temperature prediction using hybrid machine learning coupled signal decomposition: EWT versus MODWT. Ecological Informatics. 78. 102376–102376. 17 indexed citations

Yang, Cheng‐Xue, et al.. (2023). The Coorg Block, southern India: Insights from felsic and mafic magmatic suites on Mesoarchean plate tectonics and correlation with supercontinent Ur. Gondwana Research. 118. 1–36. 9 indexed citations

Kim, Sungwon, Youngmin Seo, Anurag Malik, et al.. (2023). Quantification of river total phosphorus using integrative artificial intelligence models. Ecological Indicators. 153. 110437–110437. 12 indexed citations

10.

Ghorbani, Mohammad Ali, Sujay Raghavendra Naganna, Sungwon Kim, et al.. (2022). Integration of Multiple Models with Hybrid Artificial Neural Network‐Genetic Algorithm for Soil Cation‐Exchange Capacity Prediction. Complexity. 2022(1). 3 indexed citations

11.

Heddam, Salim, Sungwon Kim, Ali Danandeh Mehr, et al.. (2022). Bat algorithm optimised extreme learning machine (Bat‐ELM): A novel approach for daily river water temperature modelling. Geographical Journal. 189(1). 78–89. 16 indexed citations

12.

Kim, Sungwon, et al.. (2021). AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translate. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 1–14. 21 indexed citations

13.

Melesse, Assefa M., Khabat Khosravi, John P. Tiefenbacher, et al.. (2020). River Water Salinity Prediction Using Hybrid Machine Learning Models. Water. 12(10). 2951–2951. 91 indexed citations

14.

Kim, Sungwon, et al.. (2020). Implementation on the evolutionary machine learning approaches for streamflow forecasting: case study in the Seybous River, Algeria. Journal of Korea Water Resources Association. 53(6). 395–408. 4 indexed citations

15.

Ghorbani, Mohammad Ali, Rahman Khatibi, Vijay P. Singh, et al.. (2020). Continuous monitoring of suspended sediment concentrations using image analytics and deriving inherent correlations by machine learning. Scientific Reports. 10(1). 8589–8589. 21 indexed citations

16.

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher, Isa Ebtehaj, Sungwon Kim, et al.. (2019). Novel Hybrid Data-Intelligence Model for Forecasting Monthly Rainfall with Uncertainty Analysis. Water. 11(3). 502–502. 90 indexed citations

17.

Kim, Sungwon & Hung Soo Kim. (2008). The Integrational Operation Method for the Modeling of the Pan Evaporation and the Alfalfa Reference Evapotranspiration. Journal of the Korean Society of Civil Engineers. 28. 199–213. 1 indexed citations

18.

Kim, Sungwon, et al.. (2008). Geochemical and Nd-Sr Isotope Studies for Foliated Granitoids and Mylonitized Gneisses from the Myeongho Area in Northeast Yecheon Shear Zone. Economic and Environmental Geology. 41(3). 299–314. 3 indexed citations

19.

Kim, Sungwon, et al.. (2008). Best Buffer Width of Riparian Buffer Zone using a Pilot with Different Plant Species for Reduction of Non-point Pollutant Loading. Journal of Environmental Impact Assessment. 17(1). 1–9. 1 indexed citations

20.

Kim, Sungwon. (2005). Reliability Analysis of Flood Stage Forecasting using Neural Networks Model II. Uncertainty Analysis of Input Data Information. Journal of the Korean Society of Civil Engineers. 25. 483–483. 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.

Explore authors with similar magnitude of impact