Predicting the spatial distributions of elements in former military operation area using linear and nonlinear methods across the Stavnja valley, Bosnia and Herzegovina

Abstract

This study has the purpose of developing a realistic soil prediction maps of the spatial distribution of elements by evaluating and comparing different modelling techniques: Kriging, artificial neural network‐multilayer perceptron (ANN‐MLP) and multiple polynomial regressions (MPR). The Stavnja Valley was selected as a test area due to the following reasons: (1) intensive metal ore mining and metallurgical processing; (2) peculiar geomorphological natural features; (3) regular geological setting, and (4) the remaining minefields. Geospatial parameters from digital elevation models (DEM) are used as an input to advanced prediction modelling techniques: ANN‐MLP and MPR. Soil measurements, land use data, and remote sensing are applied, developed, and finally incorporated into the models of spatial distribution in the form of 2D or 3D maps. In order to reconstruct the different processes that influenced the entire study area simultaneously, we used novel approaches to modelling. This comprehensive approach not only represents an innovation in contamination mapping, but developed prediction models also help in the reconstruction of main distribution pathways, assess the real size of the affected area, and improve the data interpretation.