Measurement error-filtered machine learning in digital soil mapping

Stephan van der Westhuizen*, Gerard B.M. Heuvelink, David P. Hofmeyr, Laura Poggio

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)


This paper presents a two-stage maximum likelihood framework to deal with measurement errors in digital soil mapping (DSM) when using a machine learning (ML) model. The framework is implemented with random forest and projection pursuit regression to illustrate two different areas of machine learning, i.e. ensemble learning with trees and feature-learning. In our proposed framework, a measurement error variance (MEV) is incorporated as a weight in the log-likelihood function so that measurements with a larger MEV receive less weight when a ML model is calibrated. We evaluate the performance of the error-filtered ML models with an error-filtered regression kriging model, in a comprehensive simulation study and in a real-world case study of Namibian data. From the results we show that prediction accuracy can be increased by using our proposed framework, especially when the MEVs are large and heterogeneous.

Original languageEnglish
Article number100572
JournalSpatial Statistics
Publication statusPublished - Mar 2022


  • Digital soil mapping
  • E-BLUP
  • Machine learning
  • Maximum likelihood
  • Measurement error
  • REML


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