Sudheer Ch
Impact in
- Environmental Engineering top 1%
- Hydrological Forecasting Using AI
- Groundwater flow and contamination studies
- Water Science and Technology top 2%
- Hydrology and Watershed Management Studies
Papers in
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- Hydrological Forecasting Using AI 15
- Groundwater flow and contamination studies 8
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- Energy Load and Power Forecasting 9
- Co-authors
- Shashi Mathur (14 shared papers)Dalibor Petković (9 shared papers)Shahaboddin Shamshirband (9 shared papers)B.K. Panigrahi (4 shared papers)Jan Adamowski (5 shared papers)Muhammad Arif (2 shared papers)Kasra Mohammadi (3 shared papers)Basant Yadav (5 shared papers)
In The Last Decade
Sudheer Ch
30 papers receiving 1.8k citations
Peers
Comparison fields: 5 of 112
- Environmental Engineering 877
- Water Science and Technology 484
- Global and Planetary Change 417
- Artificial Intelligence 561
- Electrical and Electronic Engineering 592
Countries citing papers authored by Sudheer Ch
This map shows the geographic impact of Sudheer Ch'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 Sudheer Ch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sudheer Ch more than expected).
Fields of papers citing papers by Sudheer Ch
This network shows the impact of papers produced by Sudheer Ch. 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 Sudheer Ch. The network helps show where Sudheer Ch may publish in the future.
Co-authors
The 25 scholars most cited alongside Sudheer Ch, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 219 | |
| 2 | 2015 | 163 | |
| 3 | 2013 | 138 | |
| 4 | 2014 | 130 | |
| 5 | 2012 | 126 | |
| 6 | 2015 | 106 | |
| 7 | 2013 | 92 | |
| 8 | 2014 | 77 | |
| 9 | 2015 | 75 | |
| 10 | 2015 | 70 | |
| 11 | 2016 | 70 | |
| 12 | 2017 | 65 | |
| 13 | 2012 | 62 | |
| 14 | 2019 | 61 | |
| 15 | 2016 | 58 | |
| 16 | 2016 | 55 | |
| 17 | 2015 | 54 | |
| 18 | 2017 | 41 | |
| 19 | 2012 | 41 | |
| 20 | 2015 | 34 |
About Sudheer Ch
Sudheer Ch is a scholar working on Environmental Engineering, Electrical and Electronic Engineering, Artificial Intelligence, Water Science and Technology and Civil and Structural Engineering, having authored 30 papers that have together received 1.9k indexed citations. Recurring topics across this work include Hydrological Forecasting Using AI (15 papers), Energy Load and Power Forecasting (9 papers), Groundwater flow and contamination studies (8 papers), Machine Learning and ELM (7 papers), Hydrology and Watershed Management Studies (5 papers), Water resources management and optimization (4 papers), Water Quality Monitoring Technologies (4 papers) and Neural Networks and Applications (4 papers). The work is most often cited by research in Environmental Engineering (877 citations), Water Science and Technology (484 citations), Global and Planetary Change (417 citations), Artificial Intelligence (561 citations) and Electrical and Electronic Engineering (592 citations). Sudheer Ch has collaborated with scholars based in India, Malaysia and Serbia. Frequent co-authors include Shashi Mathur, Dalibor Petković, Shahaboddin Shamshirband, B.K. Panigrahi, Jan Adamowski, Muhammad Arif, Kasra Mohammadi, Basant Yadav, Ch. Suryanarayana and Milan Gocić. Their work appears in journals such as Neurocomputing, Neural Computing and Applications, Energy, Hydrological Sciences Journal and Environmental Progress & Sustainable Energy.
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.