R. May
Impact in
- Environmental Engineering top 10%
- Hydrological Forecasting Using AI
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- Hydrology and Watershed Management Studies
Papers in
-
- Water Systems and Optimization 4
- Infrastructure Maintenance and Monitoring 2
- Geotechnical Engineering and Underground Structures 1
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- Neural Networks and Applications 3
- Co-authors
- Graeme C. Dandy (5 shared papers)Holger R. Maier (5 shared papers)T.M.K.G. Fernando (2 shared papers)David Marlow (2 shared papers)John Mashford (1 shared paper)John B. Nixon (1 shared paper)Kylie Hyde (1 shared paper)
- Journals
- Neural Networks (1 paper)Journal of Computing in Civil Engineering (1 paper)RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren (1 paper)Water Practice & Technology (1 paper)Victoria University Research Repository (Victoria University) (1 paper)
- Partner nations
- Australia
In The Last Decade
R. May
9 papers receiving 237 citations
Peers
Comparison fields: 5 of 97
- Environmental Engineering 81
- Water Science and Technology 54
- Artificial Intelligence 59
- Global and Planetary Change 39
- Health Informatics 2
Countries citing papers authored by R. May
This map shows the geographic impact of R. May'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 R. May with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites R. May more than expected).
Fields of papers citing papers by R. May
This network shows the impact of papers produced by R. May. 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 R. May. The network helps show where R. May may publish in the future.
Co-authors
The 7 scholars most cited alongside R. May, 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 | 2009 | 201 | |
| 2 | Efficient selection of inputs for artificial neural network models | 2005 | 18 |
| 3 | 2011 | 11 | |
| 4 | 2006 | 5 | |
| 5 | Exploring the impact of data splitting methods on artificial neural network models | 2012 | 5 |
| 6 | 2010 | 3 | |
| 7 | 2004 | 2 | |
| 8 | 2011 | 1 | |
| 9 | 1981 | 1 |
About R. May
R. May is a scholar working on Civil and Structural Engineering, Artificial Intelligence, Environmental Engineering, Statistical and Nonlinear Physics and Finance, having authored 9 papers that have together received 247 indexed citations. Recurring topics across this work include Water Systems and Optimization (4 papers), Neural Networks and Applications (3 papers), Infrastructure Maintenance and Monitoring (2 papers), Hydrological Forecasting Using AI (2 papers), Water resources management and optimization (1 paper), Medical and Biological Sciences (1 paper), Geotechnical Engineering and Underground Structures (1 paper) and Multi-Criteria Decision Making (1 paper). The work is most often cited by research in Environmental Engineering (81 citations), Water Science and Technology (54 citations), Artificial Intelligence (59 citations), Global and Planetary Change (39 citations) and Health Informatics (2 citations). R. May has collaborated with scholars based in Australia. Frequent co-authors include Graeme C. Dandy, Holger R. Maier, T.M.K.G. Fernando, David Marlow, John Mashford, John B. Nixon and Kylie Hyde. Their work appears in journals such as Neural Networks, Journal of Computing in Civil Engineering, RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, Water Practice & Technology and Victoria University Research Repository (Victoria University).
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.