Coevolution of machine learning and process-based modelling to revolutionize Earth and environmental sciences: a perspective

Saman Razavi*, David M. Hannah, Amin Elshorbagy, Sujay Kumar, Lucy Marshall, Dimitri P. Solomatine, Amin Dezfuli, Mojtaba Sadegh, James Famiglietti

*Corresponding author for this work

Research output: Contribution to journalComment/debatepeer-review

205 Downloads (Pure)


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.

Original languageEnglish
Article numbere14596
Number of pages7
JournalHydrological Processes
Issue number6
Early online date15 May 2022
Publication statusPublished - Jun 2022

Bibliographical note

Funding Information:
This Perspective is the outcome of a series of workshops administered under the University of Birmingham's Institute of Advanced Studies (IAS) and the University of Saskatchewan's Global Institute for Water Security (GIWS) in 2021. Saman Razavi is deeply grateful for receiving financial and logistical supports during his visit under the University of Birmingham's IAS Vanguard Fellowship. David Cunha graphically designed Figure 1.

Publisher Copyright:
© 2022 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.


  • artificial intelligence
  • deep learning
  • machine learning
  • modelling objective
  • policy support
  • predication
  • process-based modelling
  • scenarios
  • scientific discovery

ASJC Scopus subject areas

  • Water Science and Technology


Dive into the research topics of 'Coevolution of machine learning and process-based modelling to revolutionize Earth and environmental sciences: a perspective'. Together they form a unique fingerprint.

Cite this