Mirzaei, F., Amirian-Chakan, A., Taghizadeh-Mehrjardi, R., Matinfar, H. (2024). Using kNN, RF and SVM and their Combination Using GR for Soil Texture Modeling. , 38(1), 77-97. doi: 10.22092/ijsr.2024.364333.735
Fereshteh Mirzaei; Alireza Amirian-Chakan; Ruhollah Taghizadeh-Mehrjardi; Hamid Reza Matinfar. "Using kNN, RF and SVM and their Combination Using GR for Soil Texture Modeling". , 38, 1, 2024, 77-97. doi: 10.22092/ijsr.2024.364333.735
Mirzaei, F., Amirian-Chakan, A., Taghizadeh-Mehrjardi, R., Matinfar, H. (2024). 'Using kNN, RF and SVM and their Combination Using GR for Soil Texture Modeling', , 38(1), pp. 77-97. doi: 10.22092/ijsr.2024.364333.735
Mirzaei, F., Amirian-Chakan, A., Taghizadeh-Mehrjardi, R., Matinfar, H. Using kNN, RF and SVM and their Combination Using GR for Soil Texture Modeling. , 2024; 38(1): 77-97. doi: 10.22092/ijsr.2024.364333.735

Using kNN, RF and SVM and their Combination Using GR for Soil Texture Modeling

پژوهش های خاک
Article 6, Volume 38, Issue 1, August 2024, Pages 77-97    PDF (1.6 M)
Document Type: Research Paper
DOI: 10.22092/ijsr.2024.364333.735
Authors
Fereshteh Mirzaei1; Alireza Amirian-Chakan* 2; Ruhollah Taghizadeh-Mehrjardi3; Hamid Reza Matinfar4
1Ph.D Student, Department of Soil Science, Faculty of Agriculture, Lorestan University, Khorramabad, Iran
2Assitant Professor, Department of Soil Science, Faculty of Agriculture, Lorestan University, Khorramabad, Iran
3Assitant Professor, Department of Rangeland and Watersghed Management, Faculty of Agriculture and Natural Resources, University of Ardakan, Ardakan, Iran.
4Professor, Department of Soil Science, Faculty of Agriculture, Lorestan University, Khorramabad, Iran.
Abstract
Soil texture is one of the most important soil properties that govern soil physical, chemical and biological behaviors. In modeling soil textural fractions, different models are used. To combine the benefits from different models, one approach is combining their predictions. Since soil texture is a compositional data, when its fractions are estimated separately there is no guarantee that the estimates will sum to 100. Log-ratio transformations before modeling are ways to deal with the problem. Little is known about modeling transformed and untransformed (UT) soil texture data using a combination of different models. In the present study, 200 surface soil samples (0-30 cm) were collected from Kuhdasht region. Random forest (RF), k-nearest neighbors (kNN) and support vector machines (SVM) and their combination using Granger-Ramanathan (GR) method were used to model soil texture data. Additive log-ratio (alr), centroid log-ratio (clr) and isometric log-ratio (ilr) transformations were used to transform texture data. Environmental variables derived from Landsat 8 and Sentinel-2 images and a digital elevation model (DEM) were used as input for all models. Results indicated that covariates derived from DEM were more important in modeling soil texture. All models improved the estimates of soil texture fractions when alr transformed data was compared to UT, clr, and ilr transformed data. The combined model (i.e. GR) did not show superiority over other models. Using GR model RMSE values for alr, clr, ilr transformed clay data and UT were 5.07%, 4.21%, 5.81%, and 6.09%, respectively. For silt RMSE values (in the same order as clay) were 7.11%, 5.15%, 9.04%, and 6.70%, and for sand were 9.20%, 7.67%, 11.69% and 8.74%, respectively. Generally, SVM using alr transformed data showed a slightly higher potential for modeling soil texture. Generally, results indicated that combining different machine learning algorithms did not necessarily improve the estimates. Therefore, it is possible to use a single appropriate model for modeling soil texture.  
Keywords
Compositional data; Ensemble model; Log-ratio transformation; Random forest
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