Articles | Volume 24, issue 5
https://doi.org/10.5194/hess-24-2505-2020
https://doi.org/10.5194/hess-24-2505-2020
Research article
 | 
14 May 2020
Research article |  | 14 May 2020

Systematic comparison of five machine-learning models in classification and interpolation of soil particle size fractions using different transformed data

Mo Zhang, Wenjiao Shi, and Ziwei Xu

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Latest update: 18 Jun 2024
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Short summary
We systematically compared 45 models for direct and indirect soil texture classification and soil particle size fraction interpolation based on 5 machine-learning models and 3 log-ratio transformation methods. Random forest showed powerful performance in both classification of imbalanced data and regression assessment. Extreme gradient boosting is more meaningful and computationally efficient when dealing with large data sets. The indirect classification and log-ratio methods are recommended.