Giha Lee

77 papers receiving 1.7k citations

Giha Lee's Hit Papers

Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting 2019 · 633 citations
6330+2+4Years since publication200400600

Peers

Giha Lee
Comparison fields: 5 of 104
  • Water Science and Technology 646
  • Environmental Engineering 611
  • Global and Planetary Change 772
  • Management, Monitoring, Policy and Law 355
  • Atmospheric Science 383
Replace Mehdi Vafakhah with:
Mehdi Vafakhah Iran
Changhyun Jun South Korea
Vahid Moosavi Iran
Chinh Luu Vietnam
Viet‐Ha Nhu Vietnam
Gwo‐Fong Lin Taiwan
Ataollah Shirzadi Iran
Aiding Kornejady Iran
Somayeh Panahi Iran
Abhirup Dikshit Australia
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Citations per field
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Citations per year

Countries citing papers authored by Giha Lee

Since Specialization
Citations

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

Fields of papers citing papers by Giha Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Giha Lee, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Giha Lee Line = papers co-authored together Giha Lee links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 86 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting
Hit paper breakdown →
2019633
2 2021123
3 201796
4 201662
5 202050
6 202149
7 201649
8 201843
9 202040
10 201738
11 201233
12 202032
13 202027
14 202325
15 202423
16 200917
17 202417
18 201517
19 201815
20 201814

About Giha Lee

Giha Lee is a scholar working on Water Science and Technology, Global and Planetary Change, Environmental Engineering, Atmospheric Science and Soil Science, having authored 86 papers that have together received 1.7k indexed citations. Recurring topics across this work include Hydrology and Watershed Management Studies (47 papers), Flood Risk Assessment and Management (35 papers), Soil erosion and sediment transport (20 papers), Landslides and related hazards (19 papers), Hydrological Forecasting Using AI (16 papers), Precipitation Measurement and Analysis (13 papers), Hydrology and Sediment Transport Processes (13 papers) and Meteorological Phenomena and Simulations (10 papers). The work is most often cited by research in Water Science and Technology (646 citations), Environmental Engineering (611 citations), Global and Planetary Change (772 citations), Management, Monitoring, Policy and Law (355 citations) and Atmospheric Science (383 citations). Giha Lee has collaborated with scholars based in South Korea, Vietnam and Japan. Frequent co-authors include Xuan-Hien Le, Sungho Jung, Hung Viet Ho, Hyunuk An, Duc Hai Nguyen, Kwansue Jung, Minseok Kim, Massimiliano Alvioli, Giang V. Nguyen and Yeonsu Kim. Their work appears in journals such as Remote Sensing, Water, Quaternary International, Journal of Hydrologic Engineering and Journal of Hydrology Regional Studies.

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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