Flash floods are the result of climatic and hydrological extremes and are manifested by dynamic and complex processes of movement of water and sediment. They represent the most frequent and widespread natural disaster at the global level, with unwanted ecological and economic consequences. The main causes of flash floods are related to numerous meteorological and physical–geographical factors. In the territory of Serbia, flash floods represent the most common natural risk with serious consequences for people’s lives and activities. Flash flood susceptibility mapping plays a crucial role in flood risk assessment and management. The current study prepared a flood inventory using light detection and ranging (LiDAR) derived digital elevationmodel, and it used integrated tree machine learning models (random forest [RF], classification and regression trees [CART], and support vector machine [SVM]) to predict flood susceptibility in the Ljuboviđa watershed, municipality Ljubovija, western Serbia. First, 12 independent variables were employed as conditioning factors: lithology, rainfall, land use/cover, elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power index, distance from streams, distance from roads, and normalized vegetation index. Using the well-known scikit-learn (train_test_split) Python module, the flood inventory dataset was split into 70 and 30% for training and validation, respectively. Themodels’performance was additionally assessed using the area under the curve (AUC). The results of the accuracy assessment demonstrated that the models for predicting flood susceptibility, RF, CART, and SVM, had AUC values of 0.854, 0.802, and 0.831, respectively; it means that RF had 85.4%, CART 80.2%, and SVM 83.1% chance of correctly ranking a random positive example higher than a random negative example, which represents the predictive power of the used models. When it came to predicting flood susceptibility, the RF model outperformed the other models used. This model estimates that 15.49, 16.04, 15.67, 23.10, and 29.70% of the watershed are very low, low, moderate, high, and extremely highly susceptible to floods, respectively. Thus, our study shows that data produced from LiDAR is potentially helpful in managing flood risk, particularly when assessing flood-related issues in the future. Flash-flood susceptibility maps have become a vital tool for risk prevention and management for government and local authorities (particularly national and local civil protection agencies, urban planning and land management departments, Ministries of Water and Environment), emergency response services (police, fire, and medical services), infrastructure and utilities sectors, insurance companies, and others.

This study introduces the Normalized Difference Gamma Ray Index (NDGRI), a novel spectral composite derived from Sentinel 2 imagery for mapping elevated natural gamma radiation in semi-arid and arid basins. We hypothesized that water-sensitive spectral indices correlate with gamma-ray hotspots in arid regions of Mongolia, where natural radionuclide distribution is influenced by hydrological processes. Leveraging historical car-borne gamma spectrometry data collected in 2008 across the Sainshand and Zuunbayan uranium project areas, we evaluated twelve spectral bands and five established moisture-sensitive indices against radiation heatmaps in Naarst and Zuunbayan. Using Pearson and Spearman correlations alongside two percentile-based overlap metrics, indices were weighted to yield a composite performance score. The best performing indices (MI—Moisture Index and NDSII_1—Normalized Difference Snow and Ice Index) guided the derivation of ten new ND constructs incorporating SWIR bands (B11, B12) and visible bands (B4, B8A). The top performer, NDGRI = (B4 − B12)/(B4 + B12) achieved a precision of 62.8% for detecting high gamma-radiation areas and outperformed benchmarks of other indices. We established climatological screening criteria to ensure NDGRI reliability. Validation at two independent sites (Erdene, Khuvsgul) using 2008 airborne gamma ray heatmaps yielded 76.41% and 85.55% spatial overlap accuracy, respectively. Our results demonstrate that NDGRI effectively delineates gamma radiation hotspots where moisture-controlled spectral contrasts prevail. The index’s stringent acquisition constraints, however, limit the temporal availability of usable scenes. NDGRI offers a rapid, cost-effective remote sensing tool to prioritize ground surveys in uranium prospective basins and may be adapted for other radiometric applications in semi-arid and arid regions.

In a previous study, the Normalized Difference Gamma-Ray Index (NDGRI) was introduced as a spectral approach to highlight surface conditions associated with elevated gamma radiation. When validated against car-borne and airborne spectrometry surveys, NDGRI demonstrated potential for delineating radiation anomalies in semi-arid terrains. Detecting such anomalies from satellite data is of practical importance, as it offers a cost-effective way to narrow down prospective zones before deploying expensive car-borne or airborne surveys. Building on this foundation, this study assesses whether deep learning can extend this approach beyond index-based methods by classifying elevated radiation from Sentinel-2 spectral data. Multilayer perceptron (MLP) networks were implemented for per-pixel spectral classification, using fully connected dense layers with dropout regularization. Hyperparameters, including number of layers, neurons per layer, dropout rates, optimizers, and learning rates, were tuned via Keras Tuner and AutoKeras in Python to balance model complexity and generalization. Training and validation samples were extracted from car-borne and air-borne gamma-ray spectrometry heatmaps, providing pixel-level labels for supervised learning. Three input configurations were tested: NDGRI provided as a single-band raster, the full set of 11 Sentinel-2 bands, and selected band subsets that showed diagnostic potential in our previous NDGRI analysis. The results showed that the NDGRI raster performed strongly as a compact predictor of elevated radiation, whereas multi-band inputs provided better overall performance, with certain subsets consistently outperforming NDGRI alone. These findings indicate that although NDGRI efficiently captures key spectral contrasts, the inclusion of targeted spectral combinations enables the network to exploit complementary features and improve classification robustness. The findings suggest that deep learning can serve as a powerful extension of NDGRI-based workflows, combining the interpretability of targeted indices with the flexibility of data-driven classification. This approach could offer a scalable and computationally efficient means of mapping elevated gamma-radiation anomalies from multispectral satellite imagery, with implications for cost-effective uranium exploration in semi-arid terrains.

Uranium exploration in southeastern Mongolia remains constrained by fragmented Soviet-era datasets and limited modern synthesis. This study addresses the problem of integrating historical geological records with contemporary exploration methods to evaluate uranium mineralization potential. A comprehensive GIS-based database was compiled from Soviet reports legally acquired from the Mineral Resources Authority of Mongolia and expanded with geological, geophysical, and drilling data collected between 2006 and 2011. Methodological advances included remote sensing detection of anomalous radioactivity in arid environments, stratigraphic modeling, and hydrogeochemical surveys. The dataset encompasses more than 1100 radioactive anomalies and approximately 300 mineralized zones, with emphasis on the Han Bogd granite massif, Ail Bayan coal deposit, Suujin Tal structural system, Zuunbayan depression, and Naarst structural complex. Results indicate that most anomalous zones are sub-economic, commonly associated with organic-rich facies such as coal seams, while the continuity of mineralized bodies remains uncertain. Nevertheless, the dual consideration of granitic source terrains and coal-bearing sedimentary traps provides new insights into uranium mobility and deposition. The significance of this work lies in its systematic integration of historical and modern data, offering a refined geological framework and highlighting key areas for future investigation, thereby contributing to ongoing discussions on sedimentary uranium resources in Mongolia. Results indicate that most anomalous zones are sub-economic, commonly associated with organic-rich facies such as coal seams, while the continuity of mineralized bodies remains uncertain. Importantly, the study highlights granitic intrusions and volcanic complexes as the primary uranium sources, with coal-bearing and sedimentary basins acting as secondary depositional environments. The dual consideration of source terrains and depositional traps provides new insights into uranium mobility and deposition.

This study introduces the Normalized Difference Gamma Ray Index (NDGRI), a novel spectral composite derived from Sentinel 2 imagery for mapping elevated natural gamma radiation in semi arid and arid basins. We hypothesized that water‐sensitive spectral indices correlate with gamma‐ray hotspots in arid regions of Mongolia, where natural radionuclide distribution is influenced by hydrological processes. Leveraging historical car-borne gamma spectrometry data collected in 2008 across the Sainshand and Zuunbayan uranium project areas, we evaluated twelve spectral bands and five established moisture-sensitive indices against radiation heatmaps in Naarst and Zuunbayan. Using Pearson and Spearman correlations alongside two percentile based overlap metrics, indices were weighted to yield a composite performance score. The best performing indices (MI - Moisture Index and NDSII_1 - Normalized Difference Snow and Ice Index) guided the derivation of ten new ND constructs incorporating SWIR bands (B11, B12) and visible bands (B4, B8A). The top performer, NDGRI = (B4–B12)/(B4 + B12) achieved a precision of 62.8% for detecting high gamma‐radiation areas and outperformed benchmarks of other indices. We established climatological screening criteria to ensure NDGRI reliability. Validation at two independent sites (Erdene, Khuvsgul) using 2008 airborne gamma ray heatmaps yielded 68.1% and 45.4% overlap, respectively. Our results demonstrate that NDGRI effectively delineates gamma radiation hotspots where moisture controlled spectral contrasts prevail. The index’s stringent acquisition constraints, however, limit temporal availability of usable scenes. NDGRI offers a rapid, cost effective remote sensing tool to prioritize ground surveys in uranium prospective basins and may be adapted for other radiometric applications in semi arid and arid regions.