Articles | Volume 24, issue 5
Hydrol. Earth Syst. Sci., 24, 2505–2526, 2020
https://doi.org/10.5194/hess-24-2505-2020
Hydrol. Earth Syst. Sci., 24, 2505–2526, 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 et al.

Download

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (further review by editor) (13 Mar 2020) by Alberto Guadagnini
AR by Wenjiao Shi on behalf of the Authors (14 Mar 2020)  Author's response    Manuscript
ED: Publish subject to technical corrections (26 Mar 2020) by Alberto Guadagnini
AR by Wenjiao Shi on behalf of the Authors (02 Apr 2020)  Author's response    Manuscript
Download
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.