Advances in the development of gamma-ray spectrometers have resulted in devices that are ideal for use in conjunction with the increasingly reliable systems of autonomously flying uncrewed aerial vehicles (UAVs) that have recently become available on the market. Airborne gamma-ray spectrometry (GRS) measurements have many different applications. Here, the technique is applied to a former uranium mining and processing site, which is characterized by relatively low specific activities and, hence, low count rates, requiring relatively large detectors and correspondingly big size UAVs. The future acceptance of the use of such UAV-based GRS systems for radionuclide mapping depends on their ability to measure absolute specific activities of natural radionuclides such as U-238 in near-surface soil that are consistent with the results of established and proven ground-based systems. To determine absolute specific activities on the ground, the gamma radiation data from airborne detectors must be corrected for attenuation caused by the flight altitude above ground. In recent years, mathematical procedures for altitude correction have been developed, that are specifically tailored to the working range of several tens of meters typical for UAVs. However, very limited experimental validation of these theoretical approaches is available. A very large dataset consisting of about 3000 UAV-based and 19,000 backpack-based measurements was collected at a low-grade uranium ore dump in Yangiabad, Uzbekistan. We applied different geostatistical interpolation methods to compare the data from both survey techniques by upscaling backpack data to airborne data. Compared to backpack systems, UAV-based systems have lower spatial resolution, so measurements average over larger areal units (or in geostatistical terminology: "spatial support"). Taking into account the change in spatial support, we illustrate that (1) the UAV-based measurements show good agreement with the upscaled backpack measurements and that (2) UAV surveys provide good delineation of contrasts of the relatively smooth U-238 specific activity distribution typical for former uranium mining and processing sites. We are able to show that the resolution of UAV-based systems is sufficient to map extended uranium waste facilities.

State-of-the-art hydrogeological investigations use transient calibrated numerical flow and transport models for multiple scenario analyses. However, the transient calibration of numerical flow and transport models still requires consistent long-term groundwater time series, which are often not available or contain data gaps, thus reducing the robustness and confidence of the numerical model. This study presents a data-driven approach for the reconstruction and prediction of gaps in a discontinuous groundwater level time series at a monitoring station in the Allertal (Saxony-Anhalt, Germany). Deep Learning and classical machine learning (ML) approaches (artificial neural networks (TensorFlow, PyTorch), the ensemble method (Random Forest), boosting method (eXtreme gradient boosting (XGBoost)), and Multiple Linear Regression) are used. Precipitation and groundwater level time series from two neighboring monitoring stations serve as input data for the prediction and reconstruction. A comparative analysis shows that the input data from one measuring station enable the reconstruction and prediction of the missing groundwater levels with good to satisfactory accuracy. Due to a higher correlation between this station and the station to be predicted, its input data lead to better adapted models than those of the second station. If the time series of the second station are used as model inputs, the results show slightly lower correlations for training, testing and, prediction. All machine learning models show a similar qualitative behavior with lower fluctuations during the hydrological summer months. The successfully reconstructed and predicted time series can be used for transient calibration of numerical flow and transport models in the Allertal (e.g., for the overlying rocks of the Morsleben Nuclear Waste Repository). This could lead to greater acceptance, reliability, and confidence in further numerical studies, potentially addressing the influence of the overburden acting as a barrier to radioactive substances.