Articles | Volume 24, issue 5
https://doi.org/10.5194/hess-24-2505-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-2505-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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
Key Laboratory of Land Surface Pattern and Simulation, State Key
Laboratory of Resources and Environmental Information System, Institute of
Geographic Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing 100101, China
School of Earth Sciences and Resources, China University of
Geosciences, Beijing 100083, China
Wenjiao Shi
CORRESPONDING AUTHOR
Key Laboratory of Land Surface Pattern and Simulation, State Key
Laboratory of Resources and Environmental Information System, Institute of
Geographic Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Ziwei Xu
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
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Cited
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- Proportional allocation with soil depth improved mapping soil organic carbon stocks M. Zhang et al. 10.1016/j.still.2022.105519
- Digital mapping of soil pH and carbonates at the European scale using environmental variables and machine learning Q. Lu et al. 10.1016/j.scitotenv.2022.159171
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- Quantifying soil properties relevant to soil organic carbon biogeochemical cycles by infrared spectroscopy: The importance of compositional data analysis P. Zhao et al. 10.1016/j.still.2023.105718
- Remote sensing and GIS for urbanization and flood risk assessment in Phnom Penh, Cambodia N. Thanh Son et al. 10.1080/10106049.2021.1941307
- Simple Optimal Sampling Algorithm to Strengthen Digital Soil Mapping Using the Spatial Distribution of Machine Learning Predictive Uncertainty: A Case Study for Field Capacity Prediction H. Yang et al. 10.3390/land11112098
- Use of machine learning for classification of sand particles L. Li & M. Iskander 10.1007/s11440-021-01443-y
- Coping with imbalanced data problem in digital mapping of soil classes A. Sharififar & F. Sarmadian 10.1111/ejss.13368
- Is macroporosity controlled by complexed clay and soil organic carbon? A. Koop et al. 10.1016/j.geoderma.2023.116565
- Mapping subaerial sand-gravel-cobble fluvial sediment facies using airborne lidar and machine learning R. Díaz Gómez et al. 10.1016/j.geomorph.2021.108106
- Machine Learning With GA Optimization to Model the Agricultural Soil-Landscape of Germany: An Approach Involving Soil Functional Types With Their Multivariate Parameter Distributions Along the Depth Profile M. Ließ et al. 10.3389/fenvs.2021.692959
- Predicting the compressive strength of steelmaking slag concrete with machine learning – Considerations on developing a mix design tool R. Penido et al. 10.1016/j.conbuildmat.2022.127896
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- Progress on spatial prediction methods for soil particle-size fractions W. Shi & M. Zhang 10.1007/s11442-023-2142-6
- Spatial mapping of hydrologic soil groups using machine learning in the Mediterranean region E. Faouzi et al. 10.1016/j.catena.2023.107364
- Digital mapping of soil-texture classes in Batifa, Kurdistan Region of Iraq, using machine-learning models B. Yousif et al. 10.1007/s12145-023-01005-8
23 citations as recorded by crossref.
- Prediction of Aquatic Ecosystem Health Indices through Machine Learning Models Using the WGAN-Based Data Augmentation Method S. Lee et al. 10.3390/su131810435
- Comparing direct and indirect approaches to predicting soil texture class D. Saurette 10.1139/cjss-2022-0040
- Digital mapping for soil texture class prediction in northwestern Türkiye by different machine learning algorithms F. Kaya et al. 10.1016/j.geodrs.2022.e00584
- Identifying the Minimum Number of Flood Events for Reasonable Flood Peak Prediction of Ungauged Forested Catchments in South Korea H. Yang et al. 10.3390/f14061131
- Effects of flooding on pavement performance: a machine learning-based network-level assessment M. Shariatfar et al. 10.1080/23789689.2021.2017736
- Assessing spatially heterogeneous scale representation with applied digital soil mapping D. Newman et al. 10.1016/j.envsoft.2022.105612
- Proportional allocation with soil depth improved mapping soil organic carbon stocks M. Zhang et al. 10.1016/j.still.2022.105519
- Digital mapping of soil pH and carbonates at the European scale using environmental variables and machine learning Q. Lu et al. 10.1016/j.scitotenv.2022.159171
- Assessing machine learning techniques for detailing soil map in the semiarid tropical region D. Cahyana et al. 10.1088/1755-1315/648/1/012018
- Integration of Sentinel-1/2 and topographic attributes to predict the spatial distribution of soil texture fractions in some agricultural soils of western Iran K. Azizi et al. 10.1016/j.still.2023.105681
- Quantifying soil properties relevant to soil organic carbon biogeochemical cycles by infrared spectroscopy: The importance of compositional data analysis P. Zhao et al. 10.1016/j.still.2023.105718
- Remote sensing and GIS for urbanization and flood risk assessment in Phnom Penh, Cambodia N. Thanh Son et al. 10.1080/10106049.2021.1941307
- Simple Optimal Sampling Algorithm to Strengthen Digital Soil Mapping Using the Spatial Distribution of Machine Learning Predictive Uncertainty: A Case Study for Field Capacity Prediction H. Yang et al. 10.3390/land11112098
- Use of machine learning for classification of sand particles L. Li & M. Iskander 10.1007/s11440-021-01443-y
- Coping with imbalanced data problem in digital mapping of soil classes A. Sharififar & F. Sarmadian 10.1111/ejss.13368
- Is macroporosity controlled by complexed clay and soil organic carbon? A. Koop et al. 10.1016/j.geoderma.2023.116565
- Mapping subaerial sand-gravel-cobble fluvial sediment facies using airborne lidar and machine learning R. Díaz Gómez et al. 10.1016/j.geomorph.2021.108106
- Machine Learning With GA Optimization to Model the Agricultural Soil-Landscape of Germany: An Approach Involving Soil Functional Types With Their Multivariate Parameter Distributions Along the Depth Profile M. Ließ et al. 10.3389/fenvs.2021.692959
- Predicting the compressive strength of steelmaking slag concrete with machine learning – Considerations on developing a mix design tool R. Penido et al. 10.1016/j.conbuildmat.2022.127896
- Cropland Expansion Mitigates the Supply and Demand Deficit for Carbon Sequestration Service under Different Scenarios in the Future—The Case of Xinjiang M. Shi et al. 10.3390/agriculture12081182
- Combining Digital Covariates and Machine Learning Models to Predict the Spatial Variation of Soil Cation Exchange Capacity F. Kaya et al. 10.3390/land12040819
- Progress on spatial prediction methods for soil particle-size fractions W. Shi & M. Zhang 10.1007/s11442-023-2142-6
- Spatial mapping of hydrologic soil groups using machine learning in the Mediterranean region E. Faouzi et al. 10.1016/j.catena.2023.107364
Latest update: 25 Sep 2023
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.
We systematically compared 45 models for direct and indirect soil texture classification and...
