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Articles | Volume 10, issue 2
Articles | Volume 10, issue 2
https://doi.org/10.5194/soil-10-679-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-10-679-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 2
Original research article
 | 
30 Sep 2024
Original research article |  | 30 Sep 2024

Insights into the prediction uncertainty of machine-learning-based digital soil mapping through a local attribution approach

Jeremy Rohmer, Stephane Belbeze, and Dominique Guyonnet

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Short summary
Machine learning (ML) models have become key ingredients for digital soil mapping. To explain why the ML model is reliable, we apply a popular method from explainable artificial intelligence to the uncertainty prediction, with an application to the mapping of hydrocarbon pollutants on urban soil. We show the benefit of a joint analysis of the influence on the best estimate and the uncertainty to improve communication with end users and support decisions regarding covariates’ characterisation.
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