Robert J. May

1.1k total citations
9 papers, 565 citations indexed

About

Robert J. May is a scholar working on Environmental Engineering, Electrical and Electronic Engineering and Water Science and Technology. According to data from OpenAlex, Robert J. May has authored 9 papers receiving a total of 565 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Environmental Engineering, 4 papers in Electrical and Electronic Engineering and 3 papers in Water Science and Technology. Recurrent topics in Robert J. May's work include Hydrological Forecasting Using AI (6 papers), Neural Networks and Applications (2 papers) and Water Systems and Optimization (2 papers). Robert J. May is often cited by papers focused on Hydrological Forecasting Using AI (6 papers), Neural Networks and Applications (2 papers) and Water Systems and Optimization (2 papers). Robert J. May collaborates with scholars based in Australia, United States and South Africa. Robert J. May's co-authors include Holger R. Maier, Graeme C. Dandy, T.M.K.G. Fernando, John B. Nixon, Wenyan Wu, David Marlow, John Mashford, Danlu Guo, Hoshin V. Gupta and Feifei Zheng and has published in prestigious journals such as Water Resources Research, Journal of Hydrology and Environmental Modelling & Software.

In The Last Decade

Robert J. May

9 papers receiving 555 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Robert J. May Australia	6	335	252	169	109	88	9	565
Keyvan Asghari Iran	10	329 1.0×	272 1.1×	151 0.9×	136 1.2×	56 0.6×	24	581
Slavco Velickov Netherlands	6	299 0.9×	211 0.8×	146 0.9×	124 1.1×	80 0.9×	9	612
Mahmud Güngör Türkiye	10	306 0.9×	228 0.9×	215 1.3×	150 1.4×	83 0.9×	20	618
Akram Seifi Iran	14	415 1.2×	325 1.3×	168 1.0×	91 0.8×	103 1.2×	28	720
Elnaz Sharghi Iran	18	408 1.2×	333 1.3×	257 1.5×	114 1.0×	89 1.0×	29	760
T.M.K.G. Fernando Australia	6	435 1.3×	320 1.3×	236 1.4×	46 0.4×	132 1.5×	9	677
Amin Mahdavi‐Meymand Iran	15	348 1.0×	232 0.9×	170 1.0×	146 1.3×	102 1.2×	40	656
Hossien Riahi-Madvar Iran	15	386 1.2×	344 1.4×	142 0.8×	178 1.6×	98 1.1×	24	776
Pin-An Chen Taiwan	8	333 1.0×	290 1.2×	236 1.4×	35 0.3×	80 0.9×	9	563
Roozbeh Moazenzadeh Iran	11	339 1.0×	248 1.0×	259 1.5×	57 0.5×	138 1.6×	15	707

Countries citing papers authored by Robert J. May

Since Specialization

Citations

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

Fields of papers citing papers by Robert J. May

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Robert J. 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 Robert J. May. The network helps show where Robert J. May may publish in the future.

Co-authorship network of co-authors of Robert J. May

This figure shows the co-authorship network connecting the top 25 collaborators of Robert J. May. A scholar is included among the top collaborators of Robert J. May 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 Robert J. May. Robert J. May is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

Chen, Junyi, Feifei Zheng, Robert J. May, et al.. (2022). Improved data splitting methods for data-driven hydrological model development based on a large number of catchment samples. Journal of Hydrology. 613. 128340–128340. 12 indexed citations

Wu, Wenyan, Robert J. May, Holger R. Maier, & Graeme C. Dandy. (2013). A benchmarking approach for comparing data splitting methods for modeling water resources parameters using artificial neural networks. Water Resources Research. 49(11). 7598–7614. 74 indexed citations

Wu, Wenyan, Robert J. May, Graeme C. Dandy, & Holger R. Maier. (2012). A method for comparing data splitting approaches for developing hydrological ANN models. ScholarsArchive (Brigham Young University). 19 indexed citations

Mashford, John, et al.. (2010). Prediction of Sewer Condition Grade Using Support Vector Machines. Journal of Computing in Civil Engineering. 25(4). 283–290. 64 indexed citations

May, Robert J., Graeme C. Dandy, Holger R. Maier, & John B. Nixon. (2008). Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems. Environmental Modelling & Software. 23(10-11). 1289–1299. 147 indexed citations

May, Robert J., Holger R. Maier, Graeme C. Dandy, & T.M.K.G. Fernando. (2008). Non-linear variable selection for artificial neural networks using partial mutual information. Environmental Modelling & Software. 23(10-11). 1312–1326. 244 indexed citations

May, Robert J., et al.. (1983). Analysis of engine usage data for tactical systems. Journal of Aircraft. 20(5). 390–396. 1 indexed citations

May, Robert J., et al.. (1983). Tactical aircraft engine usage - A statistical study. Journal of Aircraft. 20(4). 338–344. 3 indexed citations

May, Robert J., et al.. (1978). The Validity of Seven Easily Obtainable Economic and Demographic Predictors of Achievement Test Performance. Educational and Psychological Measurement. 38(2). 445–450. 1 indexed citations

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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