Ling Yang

1.1k citations
49 papers · 799 · 1 hit paper · h-index 9

Impact in

Papers in

Ling Yang

41 papers receiving 779 citations

Ling Yang's Hit Papers

A Review of the Artificial Neural Network Models for Water Quality Prediction 2020 · 319 citations
3190+2+4Years since publication100200300

Peers

Ling Yang
Comparison fields: 5 of 91
  • Water Science and Technology 437
  • Environmental Engineering 329
  • Industrial and Manufacturing Engineering 121
  • Nature and Landscape Conservation 62
  • Atmospheric Science 68
Replace A. A. Masrur Ahmed with:
A. A. Masrur Ahmed Australia
Peter Fitch Australia
C. Díaz Muñiz Spain
Jihoon Shin South Korea
Xiangyang Qi China
Samad Emamgholizadeh Iran
Hanmi Zhou China
Hamid Zare Abyaneh Iran
Miguel Ángel Pérez-Martín Spain
Lihua Song China
Ling Yang relative to A. A. Masrur Ahmed Australia A. A. Masrur Ahmed's profile →
Citations per field
00.5×3.6×
A. A. Masrur Ahmed · 1×
Citations per year

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-authors

The 25 scholars most cited alongside Ling Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ling Yang Line = papers co-authored together Ling Yang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 49 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A Review of the Artificial Neural Network Models for Water Quality Prediction
Hit paper breakdown →
2020319
2 2020186
3 201851
4 202241
5 202034
6 202319
7 201912
8 202212
9 202310
10 20228
11 20208
12 20217
13 20097
14 20227
15 20206
16 20236
17 20196
18 20245
19 20235
20 20225

About Ling Yang

Ling Yang is a scholar working on Atmospheric Science, Environmental Engineering, Water Science and Technology, Global and Planetary Change and Nature and Landscape Conservation, having authored 49 papers that have together received 799 indexed citations. Recurring topics across this work include Meteorological Phenomena and Simulations (17 papers), Precipitation Measurement and Analysis (13 papers), Water Quality Monitoring Technologies (12 papers), Soil Moisture and Remote Sensing (9 papers), Atmospheric aerosols and clouds (5 papers), Fish Ecology and Management Studies (5 papers), Water Quality Monitoring and Analysis (5 papers) and Hydrological Forecasting Using AI (4 papers). The work is most often cited by research in Water Science and Technology (437 citations), Environmental Engineering (329 citations), Industrial and Manufacturing Engineering (121 citations), Nature and Landscape Conservation (62 citations) and Atmospheric Science (68 citations). Ling Yang has collaborated with scholars based in China, United States and United Arab Emirates. Frequent co-authors include Yingyi Chen, Daoliang Li, Yeqi Liu, Lihua Song, Huihui Yu, Xiaomin Fang, Qiang Li, Yonggui Wang, Xingrong Liu and Suling Zhu. Their work appears in journals such as Earth and Space Science, Remote Sensing, Sensors, Applied Sciences and Journal of Atmospheric and Oceanic Technology.

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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