Guy Shalev
Impact in
- Environmental Engineering top 0.5%
- Hydrological Forecasting Using AI
- Soil Moisture and Remote Sensing
- Water Science and Technology top 5%
- Hydrology and Watershed Management Studies
Papers in
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- Hydrological Forecasting Using AI 6
-
- Flood Risk Assessment and Management 2
- Climate variability and models 1
- Co-authors
- Grey Nearing (5 shared papers)Frederik Kratzert (5 shared papers)Daniel Klotz (4 shared papers)Sepp Hochreiter (1 shared paper)Günter Klambauer (1 shared paper)Martin Gauch (3 shared papers)Oren Gilon (3 shared papers)Jonathan Frame (2 shared papers)
- Journals
- Hydrology and earth system sciences (5 papers)Nature (1 paper)Earth system science data (1 paper)Culture Medicine and Psychiatry (1 paper)
- Partner nations
- United StatesAustriaUnited Kingdom
In The Last Decade
Guy Shalev
8 papers receiving 1.5k citations
Guy Shalev's Hit Papers
Peers
Comparison fields: 5 of 69
- Environmental Engineering 998
- Water Science and Technology 263
- Global and Planetary Change 238
- Atmospheric Science 81
- Geology 14
Countries citing papers authored by Guy Shalev
This map shows the geographic impact of Guy Shalev'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 Guy Shalev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guy Shalev more than expected).
Fields of papers citing papers by Guy Shalev
This network shows the impact of papers produced by Guy Shalev. 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 Guy Shalev. The network helps show where Guy Shalev may publish in the future.
Co-authors
The 25 scholars most cited alongside Guy Shalev, 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 | Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets Hit paper breakdown → | 2019 | 660 |
| 2 | Deep learning rainfall–runoff predictions of extreme events Hit paper breakdown → | 2022 | 219 |
| 3 | Global prediction of extreme floods in ungauged watersheds Hit paper breakdown → | 2024 | 219 |
| 4 | Hybrid forecasting: blending climate predictions with AI models Hit paper breakdown → | 2023 | 172 |
| 5 | Flood forecasting with machine learning models in an operational framework Hit paper breakdown → | 2022 | 165 |
| 6 | 2022 | 43 | |
| 7 | 2015 | 16 | |
| 8 | 2025 | 11 |
About Guy Shalev
Guy Shalev is a scholar working on Environmental Engineering, Global and Planetary Change, Atmospheric Science, Water Science and Technology and Experimental and Cognitive Psychology, having authored 8 papers that have together received 1.5k indexed citations. Recurring topics across this work include Hydrological Forecasting Using AI (6 papers), Flood Risk Assessment and Management (2 papers), Meteorological Phenomena and Simulations (1 paper), Climate variability and models (1 paper), Hydrology and Watershed Management Studies (1 paper), Memory, Trauma, and Testimony (1 paper) and Jewish and Middle Eastern Studies (1 paper). The work is most often cited by research in Environmental Engineering (998 citations), Water Science and Technology (263 citations), Global and Planetary Change (238 citations), Atmospheric Science (81 citations) and Geology (14 citations). Guy Shalev has collaborated with scholars based in United States, Austria and United Kingdom. Frequent co-authors include Grey Nearing, Frederik Kratzert, Daniel Klotz, Sepp Hochreiter, Günter Klambauer, Martin Gauch, Oren Gilon, Jonathan Frame, Logan M. Qualls and Hoshin V. Gupta. Their work appears in journals such as Hydrology and earth system sciences, Nature, Earth system science data and Culture Medicine and Psychiatry.
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.