Levees are earth structures constructed along alluvial rivers and are considered to be one of the essential components of flood risk and natural hazard reduction. The preservation of their condition would require orderly monitoring. In Hungary, an over 4200 km long levee system was constructed from the 19th century on. Since then, many natural and anthropogenic processes, such as compaction, erosion, subsidence etc., could contribute to the slow but steady deformation of these structures. In the meantime , due to the lack of documentation, their structure and internal composition are still unclear in many sections. The present study uses different geophysical techniques to validate their efficiency in detecting the structure, composition and potential defects along a 3.6 km levee section of the Lower Tisza River, affected significantly by seepage and piping phenomena during floods. Measurements were made using Ground Penetrating Radar (GPR), Electrical Resistivity Tomography (ERT) and drillings. Information obtained by the different techniques was cross-checked and combined. This way, the potential of the applied survey strategy could be demonstrated, and the selected levee section could be assessed in terms of its structure and composition. Consequently, the major reasons for frequently occurring adverse flood phenomena at the site could be revealed. The survey approach outlined in the present paper can be applied extensively along lowland levee systems in the region and elsewhere.

Artificial levees along major rivers are critical for flood-risk mitigation, yet many aging structures have poorly constrained internal composition and material heterogeneity, limiting the reliability of conventional safety assessments. This study develops a quantitative, non-destructive framework for characterizing levee internal structure by integrating electrical resistivity tomography (ERT) with borehole (BH) observations. ERT profiles were combined with borehole measurements of grain size (D50) and water content to investigate subsurface compositional variability and to evaluate relationships between sedimentological and geophysical parameters. Grain-size data from borehole samples were modeled using four predictive approaches—random forest regression (RFR), artificial neural networks (ANN), linear regression (LR), and support vector regression (SVR)—based on ERT-derived resistivity and moisture information. The results reveal pronounced internal heterogeneity within the investigated levees and demonstrate consistent relationships between sediment composition, water content, and electrical resistivity. Among the tested models, the ensemble-based RFR provided the highest predictive performance (R² = 0.81). These findings indicate that D50 characteristics of levee materials can be reliably inferred from ERT data using machine learning, reducing the need for destructive sampling. The proposed approach offers a transferable methodology for levee assessment and supports future applications in non-destructive monitoring, spatially explicit flood-risk analysis, and climate-resilient flood-protection management.

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.

Groundwater recharge constrains sustainable aquifer use yet cannot be directly measured across entire basins. Recharge is often represented as a single recharge-potential surface, which can conflate favorable recharge conditions with the subsequent aquifer response. Here, we present a dual-indicator framework that explicitly separates climatic recharge opportunity from groundwater response. A climatic water-balance proxy (R_WB) was used to represent recharge opportunity, whereas median annual groundwater-level fluctuation (MedAF) provided an independent observation-based indicator of regional aquifer response. Machine-learning models were used to emulate the spatial organization of R_WB and to classify MedAF using climatic, soil-hydraulic, terrain, hydrogeomorphic, and land-cover predictors, with rigorous spatial validation. Applied to the Dél-Békés sector of the Maros alluvial fan (Hungary), both models achieved areas of applicability exceeding 95% of the basin, although observational support for MedAF varied with proximity to the 57 monitoring wells. Despite their physical connection, the two indicators showed limited spatial correspondence, agreeing across only 35.7% of the area. SHAP attribution for the R_WB emulator emphasized soil-hydraulic, hydrogeomorphic, and land-cover predictors, whereas the MedAF classifier was associated more strongly with climatic and topographic predictors. Applicability-constrained screening identified 100 candidate managed aquifer recharge (MAR) sites covering 18.03 km². By separating recharge opportunity from groundwater response, the framework provides a potentially transferable strategy for regional groundwater assessment and first-stage MAR screening, helping prioritize areas for subsequent site-specific investigation in heterogeneous alluvial aquifers.