Qingfang Hu

409 total citations
10 papers, 342 citations indexed

About

Qingfang Hu is a scholar working on Water Science and Technology, Global and Planetary Change and Environmental Engineering. According to data from OpenAlex, Qingfang Hu has authored 10 papers receiving a total of 342 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Water Science and Technology, 4 papers in Global and Planetary Change and 3 papers in Environmental Engineering. Recurrent topics in Qingfang Hu's work include Hydrology and Watershed Management Studies (7 papers), Flood Risk Assessment and Management (4 papers) and Hydrological Forecasting Using AI (3 papers). Qingfang Hu is often cited by papers focused on Hydrology and Watershed Management Studies (7 papers), Flood Risk Assessment and Management (4 papers) and Hydrological Forecasting Using AI (3 papers). Qingfang Hu collaborates with scholars based in China and Singapore. Qingfang Hu's co-authors include Bing Gao, Soon Keat Tan, Huan Ma, Dawen Yang, Yintang Wang, Leizhi Wang, Lingjie Li, Fan Zhang, Shuwen Wang and Linjie Li and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Hydrology and Remote Sensing.

In The Last Decade

Qingfang Hu

9 papers receiving 334 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Qingfang Hu China	7	295	251	73	66	41	10	342
Alban de Lavenne France	11	318 1.1×	251 1.0×	88 1.2×	49 0.7×	44 1.1×	27	361
Tadesse Alemayehu Netherlands	8	248 0.8×	220 0.9×	71 1.0×	42 0.6×	49 1.2×	12	330
Jianxia Chang China	8	331 1.1×	267 1.1×	98 1.3×	73 1.1×	47 1.1×	12	392
Alejandro Chamorro Germany	7	253 0.9×	201 0.8×	71 1.0×	41 0.6×	17 0.4×	12	306
Vinícius B. P. Chagas Brazil	7	253 0.9×	297 1.2×	69 0.9×	65 1.0×	24 0.6×	9	372
Cleiton da Silva Silveira Brazil	11	219 0.7×	179 0.7×	47 0.6×	42 0.6×	29 0.7×	49	342
Paolo Reggiani Germany	8	211 0.7×	251 1.0×	60 0.8×	127 1.9×	35 0.9×	17	350
Suresh Sharma United States	9	201 0.7×	212 0.8×	95 1.3×	67 1.0×	17 0.4×	30	304
Pranesh Kumar Paul India	10	212 0.7×	212 0.8×	120 1.6×	45 0.7×	44 1.1×	18	308
Wondmagegn Yigzaw United States	11	216 0.7×	259 1.0×	58 0.8×	61 0.9×	17 0.4×	15	332

Countries citing papers authored by Qingfang Hu

Since Specialization

Citations

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

Fields of papers citing papers by Qingfang Hu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qingfang Hu

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

All Works

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10 of 10 papers shown

Zhang, Fan, et al.. (2022). Performance of multiple machine learning model simulation of process characteristic indicators of different flood types. 地理科学进展. 41(7). 1239–1250. 7 indexed citations

Hu, Qingfang, et al.. (2022). Pattern of spatio-temporal variability of extreme precipitation and flood-waterlogging process in Hanjiang River basin. Atmospheric Research. 276. 106258–106258. 39 indexed citations

Yang, Mingyi, et al.. (2021). Quantitative analysis and comparative study on provincial water environment policies in the Tai Lake Basin. 资源科学. 43(10). 2042–2054.

Wang, Shuwen, et al.. (2021). Calculation of instream ecological water requirements under runoff variation conditions: Taking Xitiaoxi River in Taihu Lake Basin as an example. Journal of Geographical Sciences. 31(8). 1140–1158. 7 indexed citations

Hu, Qingfang, Yintang Wang, Hanbo Yang, et al.. (2021). Analysis on the Variation of Hydro-Meteorological Variables in the Yongding River Mountain Area Driven by Multiple Factors. Remote Sensing. 13(16). 3199–3199. 9 indexed citations

Li, Lingjie, et al.. (2020). Mid- and long-term runoff prediction based on time-varying weight combination and Bayesian correction. 地理科学进展. 39(4). 643–650. 1 indexed citations

Hu, Qingfang, et al.. (2020). Daily runoff predication using LSTM at the Ankang Station, Hanjing River. 地理科学进展. 39(4). 636–642. 8 indexed citations

Ma, Huan, Dawen Yang, Soon Keat Tan, Bing Gao, & Qingfang Hu. (2010). Impact of climate variability and human activity on streamflow decrease in the Miyun Reservoir catchment. Journal of Hydrology. 389(3-4). 317–324. 257 indexed citations

Hu, Qingfang, et al.. (2010). Tourism Information Value Structure: A Case Study from Changsha. 22. 1–4. 1 indexed citations

10.

Hu, Qingfang & Yintang Wang. (2009). Impact assessment of climate change and human activities on annual highest water level of Taihu Lake. SHILAP Revista de lepidopterología. 13 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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