Leaching large amounts of acidity and metals into recipient watercourses and estuaries, acid sulfate (a.s.) soils constitute a substantial environmental issue worldwide. Mapping of these soils enables measures to be taken to prevent pollution in high risk areas. In Denmark, legislation prohibits drainage of areas classified as potential a.s. soilswithout prior permission fromenvironmental authorities. Themapping of these soils was first conducted in the 1980’s.Wetlands, inwhich Danish potential a.s. soils mostly occur,were targeted and the soilswere surveyed through conventional mapping. In this study, a probability map for potential a.s. soil occurrence was constructed for thewetlands located in Jutland, Denmark (c. 6500 km2), using the digital soilmapping (DSM) approach. Among the variety of available DSM techniques, artificial neural networks (ANNs) were selected. More than 8000 existing soil observations and 16 environmental variables, including geology, landscape type, land use and terrain parameters,were available as input datawithin themodeling. Predictionmodels based on various network topologieswere assessed for different selections of soil observations and combinations of environmental variables. The overall prediction accuracy based on a 30% hold-back validation data reached 70%. Furthermore, the conventional map indicated 32% of the study area (c. 2100 km2) as having a high frequency for potential a.s. soils while the digital map displayed about 46% (c. 3000 km2) as high probability areas for potential a.s. soil occurrence. ANNs, thus, demonstrated promising predictive classification abilities for themapping of potential a.s. soils on a large extent.
