Advanced AI Soil Mapping Techniques and Transboundary Risk Assessment for the Drava River Floodplain

This study introduces an innovative methodology for generating realistic soil prediction maps that visualise the spatial distribution of specific chemicals, achieved through the rigorous evaluation and comparison of advanced modelling techniques, including innovative modelling techniques based on the use of neural networks and multilayer perceptrons (MLPs). The Drava River floodplain was selected as the primary case study based on stringent criteria: a) intensive historical metal ore mining and metallurgical processing activities, which have left a legacy of contamination; b) distinctive geomorphological features, such as dynamic floodplains and sediment deposition zones; and c) diverse geological settings that facilitate reliable model calibration across transboundary reaches. Soil measurements were integrated with diverse geospatial datasets—derived from Digital Elevation Models (DEMs), land cover classifications, and remote sensing imagery—to enable high-resolution mapping of contaminant distributions via sophisticated predictive modelling powered by neural networks and MLPs. A novel, holistic approach was applied to simultaneously reconstruct multiple influencing processes, including erosion, sediment transport, and pollutant dispersion, across the entire study area. This comprehensive framework not only advances contamination mapping practices but also empowers the developed models to trace primary distribution pathways, quantify the true extent of affected zones, enhance data interpretability, and inform evidence-based decisions on land-use planning, remediation strategies, and environmental management in mining-impacted regions.