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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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Cited articles

Aas, K., Jullum, M., and Løland, A.: Explaining individual predictions when features are dependent: More accurate approximations to Shapley values, Artif. Intell., 298, 103502, https://doi.org/10.1016/j.artint.2021.103502, 2021. 
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Rajendra Acharya, U., Makarenkov, V., and Nahavandi, S.: A review of uncertainty quantification in deep learning: Techniques, applications and challenges, Inform. Fusion, 76, 243–297, 2021. 
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Arrouays, D., McBratney, A., Bouma, J., Libohova, Z., Richerde-Forges, A. C., Morgan, C. L. S., Roudier, P., Poggio, L., and Mulder, V. L.: Impressions of digital soil maps: The good, the not so good, and making them ever better, Geoderma Regional, 20, e00255, https://doi.org/10.1016/j.geodrs.2020.e00255, 2020. 
Behrens, T., Schmidt, K., Viscarra Rossel, R. A., Gries, P., Scholten, T., and MacMillan, R. A.: Spatial modelling with Euclidean distance fields and machine learning, Eur. J. Soil Sci., 69, 757–770, 2018. 
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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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