Pin-An Chen
Impact in
- Environmental Engineering top 5%
- Hydrological Forecasting Using AI
- Water Science and Technology top 5%
- Hydrology and Watershed Management Studies
- Water Quality Monitoring Technologies
- Water Quality and Pollution Assessment
Papers in
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- Hydrological Forecasting Using AI 7
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- Water Quality and Pollution Assessment 3
- Hydrology and Watershed Management Studies 1
- Water Quality Monitoring Technologies 1
- Co-authors
- Fi‐John Chang (7 shared papers)Li‐Chiu Chang (3 shared papers)Eric Yi‐Hsiu Huang (1 shared paper)Yu-Hsuan Tsai (2 shared papers)G. Vachaud (2 shared papers)Alexandra Coynel (2 shared papers)Chen‐Wuing Liu (2 shared papers)Guang-Bin Huang (1 shared paper)
In The Last Decade
Pin-An Chen
9 papers receiving 554 citations
Peers
Comparison fields: 5 of 62
- Environmental Engineering 333
- Water Science and Technology 290
- Global and Planetary Change 236
- Atmospheric Science 61
- Industrial and Manufacturing Engineering 26
Countries citing papers authored by Pin-An Chen
This map shows the geographic impact of Pin-An Chen'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 Pin-An Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pin-An Chen more than expected).
Fields of papers citing papers by Pin-An Chen
This network shows the impact of papers produced by Pin-An Chen. 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 Pin-An Chen. The network helps show where Pin-An Chen may publish in the future.
Co-authors
The 14 scholars most cited alongside Pin-An Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 192 | |
| 2 | 2014 | 114 | |
| 3 | 2013 | 103 | |
| 4 | 2012 | 43 | |
| 5 | 2019 | 34 | |
| 6 | 2013 | 31 | |
| 7 | 2016 | 25 | |
| 8 | 2014 | 18 | |
| 9 | 2017 | 3 |
About Pin-An Chen
Pin-An Chen is a scholar working on Environmental Engineering, Water Science and Technology, Control and Systems Engineering, Artificial Intelligence and Electrical and Electronic Engineering, having authored 9 papers that have together received 563 indexed citations. Recurring topics across this work include Hydrological Forecasting Using AI (7 papers), Water Quality and Pollution Assessment (3 papers), Flood Risk Assessment and Management (2 papers), Hydrology and Watershed Management Studies (1 paper), Hydrology and Drought Analysis (1 paper), Water Quality Monitoring Technologies (1 paper), Neural Networks and Applications (1 paper) and Industrial Vision Systems and Defect Detection (1 paper). The work is most often cited by research in Environmental Engineering (333 citations), Water Science and Technology (290 citations), Global and Planetary Change (236 citations), Atmospheric Science (61 citations) and Industrial and Manufacturing Engineering (26 citations). Pin-An Chen has collaborated with scholars based in Taiwan, France and Singapore. Frequent co-authors include Fi‐John Chang, Li‐Chiu Chang, Eric Yi‐Hsiu Huang, Yu-Hsuan Tsai, G. Vachaud, Alexandra Coynel, Chen‐Wuing Liu, Guang-Bin Huang, Tianchi Liu and Yue Li. Their work appears in journals such as Journal of Hydrology, The Science of The Total Environment, Journal of Environmental Management, IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Industrial Electronics.
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.