the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Systematic comparison of five machine-learning methods in classification and interpolation of soil particle size fractions using different transformed data
Abstract. Soil texture and soil particle size fractions (psf) play an increasing role in physical, chemical and hydrological processes. Digital soil mapping using machine-learning methods was widely applied to generate more detailed prediction of qualitative or quantitative outputs than traditional soil-mapping methods in soil science. As compositional data, interpolation of soil psf combined with log ratio approaches was developed to improve the prediction accuracy, which also can be used to indirectly derive soil texture. However, few reports systematically analyzed and compared the classification and regression, the accuracies of original (untransformed) and log ratio approaches, and the performance of direct and indirect soil texture classification using machine-learning methods. In this total, a total of 45 evaluation models generated from five different machine-learning models combined with original and three log ratio approaches–additive log ratio, centered log ratio and isometric log ratio (ALR, CLR and ILR, respectively), to evaluate and compare the performance of soil texture classification and soil psf interpolation. The results demonstrated that log ratio approaches modified the soil sampling data more symmetrically, and with respect to soil texture classification, random forest (RF) and extreme gradient boosting (XGB) showed notable consequences. For soil psf interpolation, RF delivered the best performance among five machine-learning models with lowest root mean squared error (RMSE, sand: 15.09 %, silt: 13.86 %, clay: 6.31 %), mean absolute error (MAE, sand: 10.65 %, silt: 9.99 %, clay: 5.00 %), Aitchison distance (AD, 0.84) and standardized residual sum of squares (STRESS, 0.61), and highest coefficient of determination (R2, sand: 53.28 %, silt: 45.77 %, clay: 53.75 %). STRESS was improved using log ratio approaches, especially CLR and ILR. There is a pronounced improvement (21.3 %) in the kappa coefficient using indirect soil texture classification compared to the direct approach. Our systematic comparison helps to elucidate the processing and selection of compositional data in spatial simulation.
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SC1: 'Could the code / steps used to produce results be made available to reviewers?', Tomislav Hengl, 17 Feb 2019
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AC1: 'All R codes for the results of soil PSF interpolation and soil texture classification are available now', Wenjiao Shi, 18 Feb 2019
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SC2: 'parameters used in the ML methods matter', Yen-Sen Lu, 13 Mar 2019
- AC2: 'Adjusted parameters for the ML methods', Wenjiao Shi, 14 Mar 2019
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SC2: 'parameters used in the ML methods matter', Yen-Sen Lu, 13 Mar 2019
-
AC1: 'All R codes for the results of soil PSF interpolation and soil texture classification are available now', Wenjiao Shi, 18 Feb 2019
-
RC1: 'A comment on the uncertainty assessement and the general validity of the work', Anonymous Referee #1, 15 Mar 2019
- AC3: 'Response to the Anonymous Referee #1', Wenjiao Shi, 14 Apr 2019
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RC2: 'Questions about sampling and some recommendations to help improve the overall readability', Tomislav Hengl, 23 Apr 2019
- AC4: 'Responses to Tomislav Hengl', Wenjiao Shi, 20 May 2019
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RC3: 'Major revision required', Anonymous Referee #3, 25 Apr 2019
- AC5: 'Responses to the Anonymous Referee #3', Wenjiao Shi, 20 May 2019
-
SC1: 'Could the code / steps used to produce results be made available to reviewers?', Tomislav Hengl, 17 Feb 2019
-
AC1: 'All R codes for the results of soil PSF interpolation and soil texture classification are available now', Wenjiao Shi, 18 Feb 2019
-
SC2: 'parameters used in the ML methods matter', Yen-Sen Lu, 13 Mar 2019
- AC2: 'Adjusted parameters for the ML methods', Wenjiao Shi, 14 Mar 2019
-
SC2: 'parameters used in the ML methods matter', Yen-Sen Lu, 13 Mar 2019
-
AC1: 'All R codes for the results of soil PSF interpolation and soil texture classification are available now', Wenjiao Shi, 18 Feb 2019
-
RC1: 'A comment on the uncertainty assessement and the general validity of the work', Anonymous Referee #1, 15 Mar 2019
- AC3: 'Response to the Anonymous Referee #1', Wenjiao Shi, 14 Apr 2019
-
RC2: 'Questions about sampling and some recommendations to help improve the overall readability', Tomislav Hengl, 23 Apr 2019
- AC4: 'Responses to Tomislav Hengl', Wenjiao Shi, 20 May 2019
-
RC3: 'Major revision required', Anonymous Referee #3, 25 Apr 2019
- AC5: 'Responses to the Anonymous Referee #3', Wenjiao Shi, 20 May 2019
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Cited
3 citations as recorded by crossref.
- Unveiling flood-generating mechanisms using circular statistics-based machine learning approach without the need for discharge data during inference Z. Zhang et al. 10.2166/nh.2023.058
- An assessment of Sentinel‐1 synthetic aperture radar, geophysical and topographical covariates for estimating topsoil particle‐size fractions S. Deodoro et al. 10.1111/ejss.13414
- Prediction of soil texture using remote sensing data. A systematic review R. Mgohele et al. 10.3389/frsen.2024.1461537