Ling Peng

2.3k total citations · 2 hit papers
48 papers, 1.8k citations indexed

About

Ling Peng is a scholar working on Environmental Engineering, Organic Chemistry and Signal Processing. According to data from OpenAlex, Ling Peng has authored 48 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Environmental Engineering, 8 papers in Organic Chemistry and 6 papers in Signal Processing. Recurrent topics in Ling Peng's work include Data Management and Algorithms (6 papers), Air Quality Monitoring and Forecasting (6 papers) and Remote Sensing and LiDAR Applications (5 papers). Ling Peng is often cited by papers focused on Data Management and Algorithms (6 papers), Air Quality Monitoring and Forecasting (6 papers) and Remote Sensing and LiDAR Applications (5 papers). Ling Peng collaborates with scholars based in China, United States and Taiwan. Ling Peng's co-authors include Tianhe Chi, Li Xiang, Yuan Hu, Xiaojing Yao, Congcong Wen, Chengzeng You, Shaolong Cui, Jing Shao, Shufu Liu and Ying‐Zhong Shen and has published in prestigious journals such as PLoS ONE, The Science of The Total Environment and Scientific Reports.

In The Last Decade

Ling Peng

42 papers receiving 1.7k citations

Hit Papers

Long short-term memory neural network for air pollutant c... 2017 2026 2020 2023 2017 2018 100 200 300 400

Peers

Ling Peng
Comparison fields: 5 of 108
  • Environmental Engineering 1.1k
  • Health, Toxicology and Mutagenesis 710
  • Automotive Engineering 344
  • Atmospheric Science 226
  • Global and Planetary Change 216
Replace Tianhe Chi with:
Tianhe Chi China
Xiaojing Yao China
Feifeng Jiang Hong Kong
Yuan Hu China
Yuexiong Ding Hong Kong
Branko Kerkez United States
Quan Shi China
Rex Britter United Kingdom
Yibin Ren China
Tianhe Chi China View profile →
Citations per field, relative to Ling Peng
Ling Peng · 1×
Citations per year, relative to Ling Peng
Ling Peng · 1×

Countries citing papers authored by Ling Peng

Since Specialization
Citations

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

Fields of papers citing papers by Ling Peng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ling Peng

This figure shows the co-authorship network connecting the top 25 collaborators of Ling Peng. A scholar is included among the top collaborators of Ling Peng 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 Peng. Ling Peng 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
# Work Indexed citations
1 0
2 0
3 12
4 89
5 1
6
A novel spatiotemporal convolutional long short-term neural network for air pollution prediction breakdown →
317
7 13
8 56
9
Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation breakdown →
491
10 4
11 1
12
Spatial-temporal Features of Air Quality in Beijing City
1
13 241
14 5
15 4
16 23
17 10
18 0
19 2
20 2

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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