Modelowanie położenia jednostek kompleksów rolniczej przydatności gleb na podstawie przetwarzania ograniczonych informacji fizjograficznych i Glebowych ze zdigitalizowanych materiałów kartograficznych / Modeling the Position of Agricultural Suitability Units of Soils on the Basis of the Limited Physiographic Information Processing with Digitized Cartographic Materials

Abstract

Abstract The aim of the study was to test the ability to model soil capability units diversity of on the basis of limited information about particle size and morphology of the terrain data. The data obtained from digitization of maps of agricultural soil and topography of the region of the Upper Silesian Industrial District. Rule extraction tools and build models were algorithms in the field of computational intelligence: different versions of decision trees, neural networks and deep learning algorithms. The best algorithms allow for correct classification to 90% of the elements of the validation set. The design ensemble of specialized classifier algorithm increased the efficiency of decision-making algorithm to identify a set of validation to about 94%. Proper selection decision algorithm allows the estimation of the likelihood vector belonging to a complex object. Computational intelligence algorithms can be considered as a tool for extracting classification rules from the collection of data on soils on the local or regional level.