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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Zusammenfassung:	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 of scientific discovery and policy support. In this perspective, we assert that the cultural barriers between the ML and PBM communities limit the potential of ML, and even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often ignored, differences between ML and PBM are discussed as well as their strengths and weaknesses in light of three overarching modelling objectives in EES, (1) nowcasting and prediction, (2) scenario analysis, and (3) diagnostic learning. The paper ponders over a ‘coevolutionary’ approach to model building, shifting away from a borrowing to a co-creation culture, to develop a generation of models that leverage the unique strengths of ML such as scalability to big data and high-dimensional mapping, while remaining faithful to process-based knowledge base and principles of model explainability and interpretability, and therefore, falsifiability.
Bibliographie:	GSFC Goddard Space Flight Center ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0885-6087 1099-1085
DOI:	10.1002/hyp.14596