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Batyrkhanov, Ardak
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Electrical & Electronics Engineering

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Soil erosion analysis based on machine learning method Bolsynbek, Mukhammed; Abdikerimova, Gulzira; Serikbayeva, Sandugash; Batyrkhanov, Ardak; Shrymbay, Dana; Taszhurekova, Zhazira; Zhidekulova, Gulkiz; Shraimanova, Gulmira
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10452

Abstract

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.
Co-Authors Abdikerimova, Gulzira Bolsynbek, Mukhammed Serikbayeva, Sandugash Shraimanova, Gulmira Shrymbay, Dana Taszhurekova, Zhazira Zhidekulova, Gulkiz