Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis
202522 citationsWeiting Liu, Si Chen et al.Journal of Hydrology Regional Studiesprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Yang Chang'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 Yang Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yang Chang more than expected).
This network shows the impact of papers produced by Yang Chang. 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 Yang Chang. The network helps show where Yang Chang may publish in the future.
Co-authorship network of co-authors of Yang Chang
This figure shows the co-authorship network connecting the top 25 collaborators of Yang Chang.
A scholar is included among the top collaborators of Yang Chang 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 Yang Chang. Yang Chang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Liu, Weiting, Si Chen, Yang Chang, et al.. (2025). Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis. Journal of Hydrology Regional Studies. 58. 102227–102227.22 indexed citations breakdown →
Chen, Yining, et al.. (2018). Comparison of Flow and Energy Reduction by Representative Intertidal Plants, Southeast China.5 indexed citations
14.
Chang, Yang, et al.. (2011). Guide to the aquatic Heteroptera of Singapore and Peninsular Malaysia. VI. Mesoveliidae with description of a new Nereivelia species from Singapore. The Raffles bulletin of zoology. 59.7 indexed citations
15.
Cheng, Lanna, Yang Chang, Daiqin Li, & Hongmao Liu. (2006). AQUATIC HETEROPTERA (INSECTA: GERROMORPHA AND NEPOMORPHA) FROM XISHUANGBANNA, YUNNAN, CHINA. The Raffles bulletin of zoology. 54(2). 203–214.6 indexed citations
16.
Chang, Yang. (2005). Cloning of a cDNA encoding antifreeze protein in Microdera punctipenis dzunarica (Coleoptera: Tenebrionidae) and its activity assay. Acta Entomologica Sinica.5 indexed citations
17.
Chang, Yang. (2004). Densities and viscosities for selfassociated binary system of acetic acid water at different temperature. Chemical Engineering(China).1 indexed citations
18.
Chang, Yang, et al.. (2003). Effects of different nutrient treatments on the senescence of rice leaves.. Zhongguo shengtai nongye xuebao.1 indexed citations
19.
Chang, Yang. (2001). Determination of Diphenhydramine Hydrochloride Tablets and Injection by Fluorospectrophotometry. Chinese Journal of Pharmaceuticals.1 indexed citations
20.
Chang, Yang, et al.. (1990). A preliminary checklist of the semiaquatic and aquatic Hemiptera (Heteroptera: Gerromorpha and Nepomorpha) of Ulu Kinchin, Pahang, Malaysia.. 43(4). 282–288.3 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.