Prediction of Groundwater Changes Using Artificial Intelligence Models Based on Remote Sensing Images of Surface Water Bodies
Keywords:
Transformers, SegFormer, Remote sensing imagery, Geology, Hydrology, Hydrogeology
Abstract
The study analyzes the annual changes in surface water bodies based on multi-platform remote sensing satellite imagery and links these changes to the depletion of groundwater resources. Among transformer-based architectures, the SegFormer model was selected and trained using water body images from 2020 to 2025. The model identified a linear decreasing trend in water surface area and predicted a decline in groundwater reserves for 2026. According to the results, the water-covered area decreased by 19.6% during 2020–2025 and is expected to decline by 23.9% by 2026, with an average annual reduction rate of approximately 3.9%.
References
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Published
2026-10-29
How to Cite
Dadajonova Zilola Botirjon kizi, Khamdamov Ulugbek Salim ugli, & Nabiyev Raul Timurovich. (2026). Prediction of Groundwater Changes Using Artificial Intelligence Models Based on Remote Sensing Images of Surface Water Bodies. MATRIX Academic International Online Journal Of Engineering And Technology, 9(2), 69-77. Retrieved from https://www.maiojet.com/index.php/matrix/article/view/122
Section
Articles
