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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Bibliographic Details
Published in:Hydrological processes Vol. 36; no. 6
Main Authors: Razavi, Saman, Elshorbagy, Amin, Kumar, Sujay, Marshall, Lucy, Solomatine, Dimitri P., Dezfuli, Amin, Sadegh, Mojtaba, Famiglietti, James
Format: Journal Article
Language:English
Published: Goddard Space Flight Center Wiley 01.06.2022
John Wiley & Sons, Inc
Wiley Subscription Services, Inc
Subjects:
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 Access:Get full text
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Summary: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.
Bibliography:GSFC
Goddard Space Flight Center
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ISSN:0885-6087
1099-1085
DOI:10.1002/hyp.14596