Soil Erosion Prediction in Western Kazakhstan Through Deep Learning with a Neural Network Approach to LS-Factor Analysis

Seitkazy M. Beisekenov N. Rakhimova M. Tokbergenova A. Zulpykharov K. Kaliyeva D. Taukebayev O. Levin E.
April 2025 Springer

Journal of the Indian Society of Remote Sensing
2025 #53 Issue 4 1215 - 1226 pp.

With the rapid shifts in environmental conditions, accurately predicting soil erosion has become crucial for the sustainable management of land resources. This study introduces a deep learning-based approach to forecast soil erosion risks in Western Kazakhstan up to 2030, focusing on the LS factor defined by the Universal Soil Loss Equation (USLE). High-resolution digital elevation models (DEMs) from ASTER GDEM and historical data on climate and land use were utilized to train a convolutional neural network (CNN), enabling projections of future LS-factor changes and the corresponding erosion risks. To further improve the accuracy of LS-factor calculations, the System for Automated Geoscientific Analyses (SAGA) was applied using a multiple-flow algorithm. The results forecast a significant rise in erosion risk by 2030, with areas having LS values between 8 and 24 expected to increase by 10%, and those with LS values above 24 by 0.05%, potentially affecting an additional 24,000 km². The model achieved a 92% accuracy rate, underscoring the effectiveness of deep learning in environmental risk analysis. The integration of SAGA results provides a more detailed understanding of the erosion processes, enhancing the precision of the predictions.

Convolutional neural networks , Deep learning , Erosion risk assessment , Land management strategies , LS-factor , Western Kazakhstan

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Space Technologies, and Remote Sensing Center, Al-Farabi Kazakh National University, 71 Al-Farabi Ave, Almaty, 050040, Kazakhstan
School of Civil, Environmental and Land Management Engineering, Politecnico Di Milano, Piazza Leonardo da Vinci, 32, Milan, 20133, Italy
Graduate School of Science and Technology, Niigata University, Niigata, 950-2181, Japan
Department of Geography, Land Management, and Cadastre, Faculty of Geography and Environmental Sciences, Al-Farabi Kazakh National University, 71 Al-Farabi Ave, Almaty, 050040, Kazakhstan
Department of Cartography and Geoinformatics, Faculty of Geography and Environmental Sciences, Al-Farabi Kazakh National University, 71 Al-Farabi Ave, Almaty, 050040, Kazakhstan
School of Applied Computational Sciences, Meharry Medical College, Nashville, 37208, TN, United States

Space Technologies
School of Civil
Graduate School of Science and Technology
Department of Geography
Department of Cartography and Geoinformatics
School of Applied Computational Sciences

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