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.
Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation
2017491 citationsLi Xiang, Ling Peng et al.profile →
A novel spatiotemporal convolutional long short-term neural network for air pollution prediction
2018317 citationsCongcong Wen, Shufu Liu et al.The Science of The Total Environmentprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Tianhe Chi'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 Tianhe Chi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tianhe Chi more than expected).
This network shows the impact of papers produced by Tianhe Chi. 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 Tianhe Chi. The network helps show where Tianhe Chi may publish in the future.
Co-authorship network of co-authors of Tianhe Chi
This figure shows the co-authorship network connecting the top 25 collaborators of Tianhe Chi.
A scholar is included among the top collaborators of Tianhe Chi 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 Tianhe Chi. Tianhe Chi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wen, Congcong, Shufu Liu, Xiaojing Yao, et al.. (2018). A novel spatiotemporal convolutional long short-term neural network for air pollution prediction. The Science of The Total Environment. 654. 1091–1099.317 indexed citations breakdown →
Chi, Tianhe. (2011). China-made satellite remote-sensing integrated application service platform and its implementation. Computer Engineering and Applications Journal.
Zhang, Xin, Wen Dong, Sihai Li, Jiancheng Luo, & Tianhe Chi. (2010). China Digital Ocean Prototype System. International Journal of Digital Earth. 4(3). 211–222.14 indexed citations
14.
Chi, Tianhe. (2010). Distributed cutting algorithm of remote sensing images on Internet. Journal of Jilin University.2 indexed citations
15.
Dong, Wen, et al.. (2010). Sea-level rise prediction simulation and impact analysis information system based on 3D-GIS. Ziran zaihai xuebao. 19(2). 85–90.1 indexed citations
16.
Chi, Tianhe. (2008). System simulation test and analysis on thin-client WebGIS. Jisuanji yingyong yanjiu.3 indexed citations
17.
Chi, Tianhe. (2008). Research on data processing and application model of digital ocean. Journal of Computer Applications.1 indexed citations
18.
Chen, Huabin, Xiaodong Zhang, & Tianhe Chi. (2007). An architecture for web-based DSS. International Conference on Software Engineering. 75–79.2 indexed citations
19.
Chi, Tianhe, et al.. (2004). Application of GIS and Its Prospect in Land Suitability Assessment. Geography and Geo-Information Science.5 indexed citations
20.
Chi, Tianhe, et al.. (2003). Research and Development of Sustainable Development Information Sharing System of China.
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.