@article{Razavi-2022-Coevolution,
title = "Coevolution of machine learning and process‐based modelling to revolutionize Earth and environmental sciences: A perspective",
author = "Razavi, Saman and
Hannah, David M. and
Elshorbagy, Amin and
Kumar, Sujay V. and
Marshall, Lucy and
Solomatine, Dimitri and
Dezfuli, Amin and
Sadegh, Mojtaba and
Famiglietti, J. S.",
journal = "Hydrological Processes, Volume 36, Issue 6",
volume = "36",
number = "6",
year = "2022",
publisher = "Wiley",
url = "https://gwf-uwaterloo.github.io/gwf-publications/G22-18001",
doi = "10.1002/hyp.14596",
abstract = "Abstract 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.",
}
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<abstract>Abstract 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.</abstract>
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%0 Journal Article
%T Coevolution of machine learning and process‐based modelling to revolutionize Earth and environmental sciences: A perspective
%A Razavi, Saman
%A Hannah, David M.
%A Elshorbagy, Amin
%A Kumar, Sujay V.
%A Marshall, Lucy
%A Solomatine, Dimitri
%A Dezfuli, Amin
%A Sadegh, Mojtaba
%A Famiglietti, J. S.
%J Hydrological Processes, Volume 36, Issue 6
%D 2022
%V 36
%N 6
%I Wiley
%F Razavi-2022-Coevolution
%X Abstract 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.
%R 10.1002/hyp.14596
%U https://gwf-uwaterloo.github.io/gwf-publications/G22-18001
%U https://doi.org/10.1002/hyp.14596
Markdown (Informal)
[Coevolution of machine learning and process‐based modelling to revolutionize Earth and environmental sciences: A perspective](https://gwf-uwaterloo.github.io/gwf-publications/G22-18001) (Razavi et al., GWF 2022)
ACL
- Saman Razavi, David M. Hannah, Amin Elshorbagy, Sujay V. Kumar, Lucy Marshall, Dimitri Solomatine, Amin Dezfuli, Mojtaba Sadegh, and J. S. Famiglietti. 2022. Coevolution of machine learning and process‐based modelling to revolutionize Earth and environmental sciences: A perspective. Hydrological Processes, Volume 36, Issue 6, 36(6).
