Shengyue Chen: h-index, Total Citations, and Citation Map
Shengyue Chen's h-index is 7 (7 i10-index, 303+ total citations across 16+ publications) according to Google Scholar as of May 2026. Shengyue Chen is affiliated with Xiamen University.
Shengyue Chen is a researcher affiliated with Xiamen University, specializing in Hydroecology, Watershed Processes, Land Water Interaction. Their work has been cited 303 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Shengyue Chen's Citation Metrics
Bibliometric impact based on 16 indexed publications. Of these, 15 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 7
- i10-Index
- 7
- Total Citations
- 303
- Citing Countries
- 13
As of May 2026.
Shengyue Chen has an h-index of 7 and 303 total citations across 16 publications, with research cited by institutions in 13 countries.
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Global Impact Map
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Top Cited Works
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Improving daily streamflow simulations for data-scarce watersheds using the coupled SWAT-LSTM approach
2023128
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Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
197 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher developed a coupled SWAT-LSTM framework to enhance hydrological and water quality modeling in data-scarce environments, addressing non-stationarity and enabling explainable analysis of harmful algal blooms.
The researcher developed a machine-learning framework to analyze dynamic river water quality patterns and drivers, subsequently integrating causal inference to assess surface water quality in coastal regions.
The researcher developed a framework integrating water quality restoration costs with ecosystem service flows to quantify ecological compensation standards, demonstrated in the Taoxi Creek Watershed.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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