Ling Yang

1.1k total citations · 1 hit paper
48 papers, 746 citations indexed

About

Ling Yang is a scholar working on Atmospheric Science, Environmental Engineering and Water Science and Technology. According to data from OpenAlex, Ling Yang has authored 48 papers receiving a total of 746 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Atmospheric Science, 15 papers in Environmental Engineering and 13 papers in Water Science and Technology. Recurrent topics in Ling Yang's work include Meteorological Phenomena and Simulations (16 papers), Precipitation Measurement and Analysis (13 papers) and Water Quality Monitoring Technologies (12 papers). Ling Yang is often cited by papers focused on Meteorological Phenomena and Simulations (16 papers), Precipitation Measurement and Analysis (13 papers) and Water Quality Monitoring Technologies (12 papers). Ling Yang collaborates with scholars based in China, United States and United Arab Emirates. Ling Yang's co-authors include Yingyi Chen, Daoliang Li, Yeqi Liu, Lihua Song, Huihui Yu, Xiaomin Fang, Qiang Li, Yonggui Wang, Mingming Lu and Xingrong Liu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Cleaner Production and Scientific Reports.

In The Last Decade

Ling Yang

39 papers receiving 721 citations

Hit Papers

A Review of the Artificial Neural Network Models for Wate... 2020 2026 2022 2024 2020 100 200 300
What are hit papers?

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.

  1. A Review of the Artificial Neural Network Models for Water Quality Prediction
    2020 300 citations Yingyi Chen, Lihua Song et al. Applied Sciences profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ling Yang China 9 410 319 117 73 67 48 746
Massimiliano Lega Italy 19 83 0.2× 182 0.6× 121 1.0× 80 1.1× 34 0.5× 54 819
A. A. Masrur Ahmed Australia 15 334 0.8× 482 1.5× 74 0.6× 183 2.5× 88 1.3× 36 863
Xiangyang Qi China 10 318 0.8× 268 0.8× 116 1.0× 32 0.4× 11 0.2× 19 731
Peter Fitch Australia 10 207 0.5× 221 0.7× 45 0.4× 178 2.4× 66 1.0× 26 615
Jihoon Shin South Korea 14 180 0.4× 150 0.5× 89 0.8× 55 0.8× 18 0.3× 35 481
C. Díaz Muñiz Spain 16 255 0.6× 190 0.6× 132 1.1× 46 0.6× 11 0.2× 29 635
Jianli Ding China 18 236 0.6× 434 1.4× 79 0.7× 263 3.6× 107 1.6× 60 1.0k
Ronghui Li China 20 224 0.5× 98 0.3× 35 0.3× 305 4.2× 70 1.0× 103 1.3k
Md. Shabbir Hossain Malaysia 15 651 1.6× 539 1.7× 125 1.1× 154 2.1× 17 0.3× 24 1.1k
Fangfang Li China 18 366 0.9× 215 0.7× 14 0.1× 206 2.8× 81 1.2× 104 1.3k

Countries citing papers authored by Ling Yang

Since Specialization
Citations

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

Fields of papers citing papers by Ling Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ling Yang

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

All Works

20 of 20 papers shown
1.
Chen, Yingyi, et al.. (2025). MFSD-YOLO: A multi-scenario fish small target detection method in aquaculture. Aquacultural Engineering. 113. 102677–102677.
2.
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4.
Zhou, Xin, et al.. (2025). Analysis of the Dynamic Process of Tornado Formation on 28 July 2024. Remote Sensing. 17(15). 2615–2615.
5.
Yang, Ling, et al.. (2024). Environmental protection tax reform and corporate Climate Risk Disclosure. Journal of Cleaner Production. 478. 143931–143931. 3 indexed citations
6.
Chen, Qingqing, et al.. (2023). Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains. Remote Sensing. 15(18). 4451–4451. 5 indexed citations
7.
Yang, Ling, Yingyi Chen, Tao Shen, Huihui Yu, & Daoliang Li. (2023). A BlendMask-VoVNetV2 method for quantifying fish school feeding behavior in industrial aquaculture. Computers and Electronics in Agriculture. 211. 108005–108005. 15 indexed citations
8.
Yang, Ling, Yingyi Chen, Tao Shen, & Daoliang Li. (2023). An FSFS-Net Method for Occluded and Aggregated Fish Segmentation from Fish School Feeding Images. Applied Sciences. 13(10). 6235–6235. 5 indexed citations
9.
Yang, Ling, et al.. (2023). Water quality improvement project for initial rainwater pollution and its performance evaluation. Environmental Research. 237(Pt 1). 116987–116987. 9 indexed citations
10.
Li, Qiang, et al.. (2022). Comparative analysis of water quality prediction performance based on LSTM in the Haihe River Basin, China. Environmental Science and Pollution Research. 30(3). 7498–7509. 41 indexed citations
11.
Chen, Yi, Kadhim Hayawi, Qian Zhao, et al.. (2022). Vector Auto-Regression-Based False Data Injection Attack Detection Method in Edge Computing Environment. Sensors. 22(18). 6789–6789. 5 indexed citations
12.
Wang, Yonggui, et al.. (2022). Water-Quality Assessment and Pollution-Risk Early-Warning System Based on Web Crawler Technology and LSTM. International Journal of Environmental Research and Public Health. 19(18). 11818–11818. 7 indexed citations
13.
Li, Yu, et al.. (2020). Sensitivity Analysis on Data Time Difference for Wind Fields Synthesis of Array Weather Radar. SOLA. 16(0). 252–258. 4 indexed citations
14.
Chen, Yingyi, Lihua Song, Yeqi Liu, Ling Yang, & Daoliang Li. (2020). A Review of the Artificial Neural Network Models for Water Quality Prediction. Applied Sciences. 10(17). 5776–5776. 300 indexed citations breakdown →
15.
Yang, Ling, Yeqi Liu, Huihui Yu, et al.. (2020). Computer Vision Models in Intelligent Aquaculture with Emphasis on Fish Detection and Behavior Analysis: A Review. Archives of Computational Methods in Engineering. 28(4). 2785–2816. 170 indexed citations
16.
Ye, Kai, et al.. (2019). Fusion of High-Resolution Reflectivity for a New Array Weather Radar. Atmosphere. 10(10). 566–566. 1 indexed citations
17.
Yang, Ling, et al.. (2019). Identification and Removal of Ground Clutter Using the Fuzzy Logic Algorithm. 70. 1–3. 1 indexed citations
18.
Lei, Bo, et al.. (2019). Using convolutional neural network to classify convective cloud on radar echoes. 521. 1–3. 2 indexed citations
19.
Yang, Ling, et al.. (2019). Research on Monitoring Downburst by Using Array Weather Radar. 1–3. 1 indexed citations
20.
Yang, Ling, et al.. (2010). A New Non-rigid Image Registration Algorithm Using the Finite-Element Method. 206–210. 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.

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