Soil erosion analysis based on machine learning method
Bolsynbek M. Abdikerimova G. Serikbayeva S. Batyrkhanov A. Shrymbay D. Taszhurekova Z. Zhidekulova G. Shraimanova G.
December 2025Institute of Advanced Engineering and Science
Bulletin of Electrical Engineering and Informatics
2025#14Issue 64797 - 4811 pp.
Soil erosion poses a serious environmental and agricultural threat that undermines land productivity, sustainability, and ecosystem stability. This study develops a robust machine learning framework for predicting and analyzing soil erosion across diverse landscapes by integrating advanced remote sensing data, climate indicators, and soil characteristics. Spectral indices such as the normalized difference vegetation index (NDVI), moisture stress index (MSI), and surface albedo were employed to assess vegetation condition, moisture levels, and surface reflectance. The proposed model, based on the extreme gradient boosting (XGBoost) algorithm, classifies erosion stages with up to 99% accuracy, ranging from healthy land to severely degraded areas. The methodology includes comprehensive feature engineering, dataset preprocessing, and model evaluation. Furthermore, a comparative analysis with traditional models (USLE and RUSLE) highlights the superior predictive performance of the proposed approach. The findings offer valuable insights for sensor-based monitoring systems and cloud-based decision-support tools, supporting sustainable land use management, erosion risk mitigation, and effective soil conservation strategies.
Machine learning , Remote sensing , Soil erosion , Spectral indices , XGBoost algorithm
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Department of Information Systems, Faculty of Information Technology, L.N.Gumilyov Eurasian National University, Astana, Kazakhstan
Department of Software Engineering, Faculty of Physics, Mathematics and Information Technology, Kh. Dosmukhamedov Atyrau University, Atyrau, Kazakhstan
Department of Applied Informatics and Programming, Faculty of Technology, Taraz University named after M.Kh.Dulaty, Taraz, Kazakhstan
Department of Information Systems, Faculty of Technology, Taraz University named after M.Kh.Dulaty, Taraz, Kazakhstan
Department of Psychology, Pedagogy and Social Work, Faculty of Finance, Logistics and Digital Technologies, Karaganda University of Kazpotrebsoyuz, Karaganda, Kazakhstan
Department of Information Systems
Department of Software Engineering
Department of Applied Informatics and Programming
Department of Information Systems
Department of Psychology
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