Coevolution of Machine Learning and Process-Based Modelling to Revolutionize Earth and Environmental Sciences: A Perspective

Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process-based modelling (PBM) paradigms, which have historically been the cornerstone...

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Veröffentlicht in:	Hydrological processes Jg. 36; H. 6
Hauptverfasser:	Razavi, Saman, Elshorbagy, Amin, Kumar, Sujay, Marshall, Lucy, Solomatine, Dimitri P., Dezfuli, Amin, Sadegh, Mojtaba, Famiglietti, James
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Goddard Space Flight Center Wiley 01.06.2022 John Wiley & Sons, Inc Wiley Subscription Services, Inc
Schlagworte:	artificial intelligence Coevolution Cybernetics, Artificial Intelligence and Robotics deep learning Earth Resources and Remote Sensing Environmental science Hybridization hydrology issues and policy Knowledge bases (artificial intelligence) Learning algorithms Machine learning Modelling modelling objective Momentum Nowcasting policy support predication prediction process‐based modelling scenarios scientific discovery
ISSN:	0885-6087, 1099-1085
Online-Zugang:	Volltext
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