Greer B. Humphrey
- Environmental Engineering top 2%
- Water Science and Technology top 5%
- Global and Planetary Change top 5%
- Ocean Engineering top 10%
- Electrical and Electronic Engineering
- Co-authors
- Holger R. MaierGraeme C. DandyMatthew S. GibbsStefano GalelliAndrea CastellettiWenyan WuNick J. MountChristian W. Dawson
- Topics
- Hydrological Forecasting Using AI (8 papers)Hydrology and Watershed Management Studies (6 papers)Neural Networks and Applications (2 papers)
In The Last Decade
Greer B. Humphrey
12 papers receiving 547 citations
Peers
Comparison fields: 5 of 74
- Environmental Engineering 365
- Water Science and Technology 341
- Global and Planetary Change 268
- Ocean Engineering 75
- Electrical and Electronic Engineering 60
Countries citing papers authored by Greer B. Humphrey
This map shows the geographic impact of Greer B. Humphrey'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 Greer B. Humphrey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Greer B. Humphrey more than expected).
Fields of papers citing papers by Greer B. Humphrey
This network shows the impact of papers produced by Greer B. Humphrey. 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 Greer B. Humphrey. The network helps show where Greer B. Humphrey may publish in the future.
Co-authorship network of co-authors of Greer B. Humphrey
This figure shows the co-authorship network connecting the top 25 collaborators of Greer B. Humphrey. A scholar is included among the top collaborators of Greer B. Humphrey 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 Greer B. Humphrey. Greer B. Humphrey is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 64 | |
| 4 | 34 | |
| 5 | 2 | |
| 6 | 49 | |
| 7 | 207 | |
| 8 | A new evaluation framework for input variable selection algorithms used in environmental modelling | 3 |
| 9 | 182 | |
| 10 | Bayesian artificial neural networks : with applications water resources engineering | 2 |
| 11 | A Bayesian approach to artificial neural network model selection | 7 |
| 12 | Understanding the mechanisms modelled by artificial neural networks for hydrological prediction | 4 |
| 13 | Development of stochastic artificial neural networks for hydrological prediction | 3 |
About Greer B. Humphrey
Greer B. Humphrey is a scholar working on Environmental Engineering, Water Science and Technology and Management Science and Operations Research, having authored 13 papers that have together received 559 indexed citations. Recurring topics across this work include Hydrological Forecasting Using AI (8 papers), Hydrology and Watershed Management Studies (6 papers) and Neural Networks and Applications (2 papers). The work is most often cited by research in Environmental Engineering (365 citations), Water Science and Technology (341 citations) and Global and Planetary Change (268 citations). Greer B. Humphrey has collaborated with scholars based in Australia, Singapore and Italy. Frequent co-authors include Holger R. Maier, Graeme C. Dandy, Matthew S. Gibbs, Stefano Galelli, Andrea Castelletti, Wenyan Wu, Nick J. Mount, Christian W. Dawson, Robert J. Abrahart and Ioannis N. Athanasiadis. Their work appears in journals such as Journal of Hydrology, Hydrology and earth system sciences and Environmental Modelling & Software.
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.