Assessing the representational accuracy of data-driven models: The case of the effect of urban green infrastructure on temperature

Open access
Date
2021-07Type
- Journal Article
Abstract
Data-driven modelling with machine learning (ML) is already being used for predictions in environmental science. However, it is less clear to what extent data-driven models that successfully predict a phenomenon are representationally accurate and thus increase our understanding of the phenomenon. Besides empirical accuracy, we propose three criteria to indirectly assess the relationships learned by the ML algorithms and how they relate to a phenomenon under investigation: first, consistency of the outcomes with background knowledge; second, the adequacy of the measurements, datasets and methods used to construct a data-driven model; third, the robustness of interpretable machine learning analyses across different ML algorithms. We apply the three criteria with a case study modelling of the effect of different urban green infrastructure types on temperature and show that our approach improves the assessment of representational accuracy and reduces representational uncertainty, which can improve the understanding of modelled phenomena. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000479923Publication status
publishedExternal links
Journal / series
Environmental Modelling & SoftwareVolume
Pages / Article No.
Publisher
ElsevierSubject
Urban heat; Machine learning; Representational accuracy; Interpretable machine learning; Data-driven modellingOrganisational unit
09576 - Bresch, David Niklaus / Bresch, David Niklaus
03777 - Knutti, Reto / Knutti, Reto
Funding
167215 - Combining theory with Big Data? The case of uncertainty in prediction of trends in extreme weather and impacts (SNF)
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