Integrating Ground Penetrating Radar and machine learning for assessment of lake bed permeability and potential vertical-water-loss zones in shallow lake under climatic stress

Abstract. Climate change and increasing anthropogenic pressures have intensified the vulnerability of inland water bodies, altering their hydrological balances, reducing their water levels, and degrading their water quality. One critical issue in this context is the limited understanding of lake bed hydrogeology, particularly the extent to which sediments hinder (as aquitards) or permit subsurface leakage. Although sediment sampling provides valuable point-based information, its spatial coverage is limited, emphasizing the need for high-resolution, lake-wide geophysical methods. This study determined whether the bed of Lake Vadkerti, a shallow lake experiencing persistent water level decline, facilitates vertical water loss. An integrated method combining ground-penetrating radar (GPR) and sediment sampling was used to evaluate subsurface sediment structures. A dense grid of GPR profiles was collected, enabling 2D profile interpretation and 3D time-slice visualization. Amplitude polarity, reflector geometry, and attenuation modeling were applied to identify stratified sedimentary layers. The resulting aquitard zoning map revealed heterogeneous lake bed conditions: low-permeability aquitards dominate the central and southern areas, whereas higher-permeability non-aquitards appear along the northeastern and central-western margins, indicating potential zones of groundwater interaction. The performance of four machine learning models—K-nearest neighbors, random forest, extra trees, and gradient boosting—in classifying aquitard zones based on GPR amplitude features was evaluated. The extra trees model demonstrated the most balanced performance across all classes and stronger generalization, with 97 % accuracy and high recall across all classes (aquitard: 100 %, leaky aquitard: 86 %, non-aquitard: 79 %). Moreover, its spatial predictions were consistent with observed hydrostratigraphic patterns. This approach provides a comprehensive framework for understanding the hydrological functioning of lake beds and informing sustainable water management in climatically sensitive freshwater systems.