Craig Pelissier
- Water Science and Technology top 5%
- Environmental Engineering top 5%
- Global and Planetary Change top 10%
- Nuclear and High Energy Physics top 10%
- Atmospheric Science
- Co-authors
- Andrei AlexandruGrey NearingAlden Keefe SampsonHoshin V. GuptaFrederik KratzertDaniel KlotzJonathan FrameCristina Prieto
- Topics
- Particle physics theoretical and experimental studies (3 papers)Hydrological Forecasting Using AI (3 papers)Hydrology and Watershed Management Studies (3 papers)
- Journals
- Water Resources ResearchJournal of Computational PhysicsPhysical review. D. Particles, fields, gravitation, and cosmology
- Partner nations
- United StatesAustriaSpain
In The Last Decade
Craig Pelissier
8 papers receiving 530 citations
Hit Papers
Peers
Comparison fields: 5 of 47
- Water Science and Technology 300
- Environmental Engineering 283
- Global and Planetary Change 237
- Nuclear and High Energy Physics 131
- Atmospheric Science 52
Countries citing papers authored by Craig Pelissier
This map shows the geographic impact of Craig Pelissier'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 Craig Pelissier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Craig Pelissier more than expected).
Fields of papers citing papers by Craig Pelissier
This network shows the impact of papers produced by Craig Pelissier. 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 Craig Pelissier. The network helps show where Craig Pelissier may publish in the future.
Co-authorship network of co-authors of Craig Pelissier
This figure shows the co-authorship network connecting the top 25 collaborators of Craig Pelissier. A scholar is included among the top collaborators of Craig Pelissier 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 Craig Pelissier. Craig Pelissier is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | What Role Does Hydrological Science Play in the Age of Machine Learning?breakdown → | 391 |
| 3 | 3 | |
| 4 | 5 | |
| 5 | 1 | |
| 6 | Machine Learning for Carbon Monitoring | 0 |
| 7 | Image Registration and Data Assimilation as a QUBO on the D-Wave Quantum Annealer | 1 |
| 8 | Data Assimilation on a Quantum Annealing Computer: Feasibility and Scalability | 1 |
| 9 | 82 | |
| 10 | 16 | |
| 11 | 39 |
About Craig Pelissier
Craig Pelissier is a scholar working on Environmental Engineering, Nuclear and High Energy Physics and Water Science and Technology, having authored 11 papers that have together received 542 indexed citations. Recurring topics across this work include Particle physics theoretical and experimental studies (3 papers), Hydrological Forecasting Using AI (3 papers) and Hydrology and Watershed Management Studies (3 papers). The work is most often cited by research in Water Science and Technology (300 citations), Environmental Engineering (283 citations) and Global and Planetary Change (237 citations). Craig Pelissier has collaborated with scholars based in United States, Austria and Spain. Frequent co-authors include Andrei Alexandru, Grey Nearing, Alden Keefe Sampson, Hoshin V. Gupta, Frederik Kratzert, Daniel Klotz, Jonathan Frame, Cristina Prieto, Frank Lee and Troy Ames. Their work appears in journals such as Water Resources Research, Journal of Computational Physics and Physical review. D. Particles, fields, gravitation, and cosmology.
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.