Long Jiang

77 papers receiving 1.6k citations

Long Jiang's Hit Papers

Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model 2019 · 363 citations
3630+2+4Years since publication100200300

Peers

Long Jiang
Comparison fields: 5 of 130
  • Organic Chemistry 397
  • Inorganic Chemistry 178
  • Media Technology 98
  • Environmental Engineering 140
  • Ocean Engineering 144
Replace Yu Su with:
Yu Su China
Yuanyuan Wang China
Zhenguo Zhang China
Donghui Zhang China
Zihao Wang China
Shaohua Wu China
Zhijie Zhang China
Yinghua Wang China
Jingjing Cao China
Yoshio Fukuda Japan
Long Jiang relative to Yu Su China Yu Su's profile →
Citations per field
00.5×1.5×2.1×
Yu Su · 1×
Citations per year

Countries citing papers authored by Long Jiang

Since Specialization
Citations

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

Fields of papers citing papers by Long Jiang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Long Jiang, 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 Long Jiang Line = papers co-authored together Long Jiang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 83 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model
Hit paper breakdown →
2019363
2 2001264
3 2004119
4 202371
5 202265
6 200258
7 200256
8 200548
9 201545
10 202435
11 200935
12 200330
13 201927
14 202224
15 201122
16 202421
17 202120
18 202320
19 201020
20 202517

About Long Jiang

Long Jiang is a scholar working on Electrical and Electronic Engineering, Biomedical Engineering, Ecology, Media Technology and Atmospheric Science, having authored 83 papers that have together received 1.6k indexed citations. Recurring topics across this work include Remote Sensing in Agriculture (12 papers), Remote-Sensing Image Classification (10 papers), Remote Sensing and LiDAR Applications (8 papers), Remote Sensing and Land Use (7 papers), Hydrocarbon exploration and reservoir analysis (6 papers), Electrochemical sensors and biosensors (6 papers), Advanced biosensing and bioanalysis techniques (5 papers) and Asymmetric Hydrogenation and Catalysis (4 papers). The work is most often cited by research in Organic Chemistry (397 citations), Inorganic Chemistry (178 citations), Media Technology (98 citations), Environmental Engineering (140 citations) and Ocean Engineering (144 citations). Long Jiang has collaborated with scholars based in China, United States and Netherlands. Frequent co-authors include Kuiling Ding, Jieyu Hu, Xiaoqiang Shen, Mengmeng Li, Bao‐Ming Ji, Ziyan Cheng, Yuetian Liu, Junqiang Wang, Liang Xue and Jingzhe Zhang. Their work appears in journals such as IEEE Geoscience and Remote Sensing Letters, Colloids and Surfaces A Physicochemical and Engineering Aspects, ISPRS Journal of Photogrammetry and Remote Sensing, International Journal of Applied Earth Observation and Geoinformation and Marine and Petroleum Geology.

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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