Coevolution of machine learning and process‐based modelling to revolutionize Earth and environmental sciences: A perspective

Author:

Razavi Saman12ORCID,Hannah David M.3ORCID,Elshorbagy Amin2,Kumar Sujay4,Marshall Lucy5,Solomatine Dimitri P.678,Dezfuli Amin9,Sadegh Mojtaba10ORCID,Famiglietti James1

Affiliation:

1. Global Institute for Water Security, School of Environment and Sustainability University of Saskatchewan Saskatoon Canada

2. Department of Civil, Geological and Environmental Engineering University of Saskatchewan Saskatoon Canada

3. School of Geography, Earth and Environment Sciences University of Birmingham Birmingham UK

4. Hydrological Sciences Lab NASA Goddard Space Flight Center Greenbelt USA

5. School of Civil and Environmental Engineering University of New South Wales Kensington Australia

6. Department of Hydroinformatics and Socio‐Technical Innovation IHE Delft Institute for Water Education Delft The Netherlands

7. Water Resources Section Delft University of Technology Delft The Netherlands

8. Water Problems Institute of RAS Moscow Russia

9. Science Systems and Applications, Inc. Global Modeling and Assimilation Office, NASA Goddard Space Flight Center Greenbelt USA

10. Department of Civil Engineering Boise State University Boise USA

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Wiley

Subject

Water Science and Technology

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