Hybrid machine learning model and terrain variables for spatial modeling of topsoil physicochemical properties

Firas Aljanabi; Mert Dedeoglu

doi:10.26833/ijeg.1655607

Hybrid machine learning model and terrain variables for spatial modeling of topsoil physicochemical properties

Abstract

Soil surveys and mapping using conventional methods are time consuming and costly, particularly for large areas, while the demand for detailed soil data continues to grow for applications in land management and precision agriculture. In this study, a hybrid Random Forest Kriging (RFK) model combining the machine learning capabilities of Random Forest (RF) with the spatial prediction strength of the Kriging geostatistical model was employed alongside three other machine learning algorithms: Artificial Neural Networks (ANN), Gradient Boosting Machines (GBM), and K-Nearest Neighbor (KNN). These models were utilized to predict the spatial distribution of five soil physicochemical properties: potential of Hydrogen (pH), Electrical Conductivity (EC), Calcium Carbonate (CaCO₃), Bulk Density (BD), and Available Water Capacity (AWC) in a region located in Central Anatolia, Turkey. The physicochemical properties of 119 soil samples collected from the study area were analyzed. One of the objectives of this study was to evaluate the effectiveness of low-scale terrain variables as predictors for soil property estimation. For this purpose, three auxiliary terrain variables digital elevation model (DEM), slope, and topographic roughness index (TRI) were derived from remote sensing data and analyzed using GIS techniques. The predicted maps were evaluated utilizing three statistical metrics: Concordance Correlation Coefficient (CCC), root mean square error (RMSE), and mean absolute error (MAE). The results showed that the hybrid RFK model outperformed other machine learning models in predicting soil properties, achieving the best CCC values and the lowest RMSE and MAE, while the performance of the other models remained relatively similar and weak. Furthermore, the findings indicated that the hybrid model's ability to delineate the spatial patterns of soil properties was primarily influenced by elevation and slope, despite the moderate effectiveness of low-scale terrain variables as auxiliary inputs in digital soil mapping.

Keywords

References

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Details

Primary Language

English

Subjects

Land Management, Cartography and Digital Mapping, Geographical Information Systems (GIS) in Planning

Journal Section

Research Article

Authors

Firas Aljanabi ^*
0000-0002-5122-3699
Türkiye

Mert Dedeoglu
0000-0001-8611-3724
Türkiye

Early Pub Date

August 25, 2025

Publication Date

October 1, 2025

Submission Date

March 11, 2025

Acceptance Date

May 7, 2025

Published in Issue

Year 2026 Volume: 11 Number: 1

DOI

https://doi.org/10.26833/ijeg.1655607

IZ

https://izlik.org/JA42KC34DG

Cite

RIS / Bibtex

APA

Aljanabi, F., & Dedeoglu, M. (2025). Hybrid machine learning model and terrain variables for spatial modeling of topsoil physicochemical properties. International Journal of Engineering and Geosciences, 11(1), 149-162. https://doi.org/10.26833/ijeg.1655607

AMA

1.Aljanabi F, Dedeoglu M. Hybrid machine learning model and terrain variables for spatial modeling of topsoil physicochemical properties. IJEG. 2025;11(1):149-162. doi:10.26833/ijeg.1655607

Chicago

Aljanabi, Firas, and Mert Dedeoglu. 2025. “Hybrid Machine Learning Model and Terrain Variables for Spatial Modeling of Topsoil Physicochemical Properties”. International Journal of Engineering and Geosciences 11 (1): 149-62. https://doi.org/10.26833/ijeg.1655607.

EndNote

Aljanabi F, Dedeoglu M (October 1, 2025) Hybrid machine learning model and terrain variables for spatial modeling of topsoil physicochemical properties. International Journal of Engineering and Geosciences 11 1 149–162.

IEEE

[1]F. Aljanabi and M. Dedeoglu, “Hybrid machine learning model and terrain variables for spatial modeling of topsoil physicochemical properties”, IJEG, vol. 11, no. 1, pp. 149–162, Oct. 2025, doi: 10.26833/ijeg.1655607.

ISNAD

Aljanabi, Firas - Dedeoglu, Mert. “Hybrid Machine Learning Model and Terrain Variables for Spatial Modeling of Topsoil Physicochemical Properties”. International Journal of Engineering and Geosciences 11/1 (October 1, 2025): 149-162. https://doi.org/10.26833/ijeg.1655607.

JAMA

1.Aljanabi F, Dedeoglu M. Hybrid machine learning model and terrain variables for spatial modeling of topsoil physicochemical properties. IJEG. 2025;11:149–162.

MLA

Aljanabi, Firas, and Mert Dedeoglu. “Hybrid Machine Learning Model and Terrain Variables for Spatial Modeling of Topsoil Physicochemical Properties”. International Journal of Engineering and Geosciences, vol. 11, no. 1, Oct. 2025, pp. 149-62, doi:10.26833/ijeg.1655607.

Vancouver

1.Firas Aljanabi, Mert Dedeoglu. Hybrid machine learning model and terrain variables for spatial modeling of topsoil physicochemical properties. IJEG. 2025 Oct. 1;11(1):149-62. doi:10.26833/ijeg.1655607