Lirong Yin
- Environmental Engineering top 0.5%
- Air Quality Monitoring and Forecasting 12
- Hydrological Forecasting Using AI 6
- Artificial Intelligence top 0.5%
- Advanced Technologies in Various Fields 38
- Topic Modeling 8
- Domain Adaptation and Few-Shot Learning 7
- Global and Planetary Change top 2%
- Flood Risk Assessment and Management 11
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- Air Quality and Health Impacts 13
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- Multimodal Machine Learning Applications 10
- Partner nations
- ChinaUnited StatesFrance
In The Last Decade
Lirong Yin
122 papers receiving 7.3k citations
Hit Papers
Peers
Comparison fields: 5 of 187
- Environmental Engineering 1.1k
- Artificial Intelligence 2.3k
- Global and Planetary Change 1.0k
- Health, Toxicology and Mutagenesis 592
- Computer Vision and Pattern Recognition 864
Countries citing papers authored by Lirong Yin
This map shows the geographic impact of Lirong Yin'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 Lirong Yin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lirong Yin more than expected).
Fields of papers citing papers by Lirong Yin
This network shows the impact of papers produced by Lirong Yin. 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 Lirong Yin. The network helps show where Lirong Yin may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Lirong Yin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 4 | |
| 2 | 2024 | 50 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 15 | |
| 5 | 2024 | 1 | |
| 6 | 2023 | 38 | |
| 7 | Predict the effect of meteorological factors on haze using BP neural networkbreakdown → | 2023 | 99 |
| 8 | YOLOV4_CSPBi: Enhanced Land Target Detection Modelbreakdown → | 2023 | 100 |
| 9 | 2023 | 47 | |
| 10 | U-Net-STN: A Novel End-to-End Lake Boundary Prediction Modelbreakdown → | 2023 | 126 |
| 11 | 2023 | 47 | |
| 12 | 2022 | 58 | |
| 13 | 2022 | 54 | |
| 14 | 2022 | 82 | |
| 15 | 2022 | 63 | |
| 16 | 2022 | 65 | |
| 17 | 2022 | 11 | |
| 18 | 2021 | 16 | |
| 19 | 2021 | 24 | |
| 20 | 2021 | 89 |
About Lirong Yin
Lirong Yin is a scholar working on Artificial Intelligence, Environmental Engineering and Computer Vision and Pattern Recognition, having authored 122 papers that have together received 7.5k indexed citations. Recurring topics across this work include Advanced Technologies in Various Fields (38 papers), Air Quality and Health Impacts (13 papers), Air Quality Monitoring and Forecasting (12 papers), Flood Risk Assessment and Management (11 papers), Multimodal Machine Learning Applications (10 papers), Topic Modeling (8 papers), Domain Adaptation and Few-Shot Learning (7 papers) and Hydrological Forecasting Using AI (6 papers). The work is most often cited by research in Environmental Engineering (1.1k citations), Artificial Intelligence (2.3k citations) and Global and Planetary Change (1.0k citations). Lirong Yin has collaborated with scholars based in China, United States and France. Frequent co-authors include Wenfeng Zheng, Zhengtong Yin, Bo Yang, Shan Liu, Mingzhe Liu, Jiawei Tian, Xuan Liu, Xiaolu Li, Zhixin Liu and Siyu Lu. Their work appears in journals such as Applied Sciences, Atmosphere, Electronics, Computer Modeling in Engineering & Sciences and Urban Climate.
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.