Search Results

You are looking at 1 - 2 of 2 items for

  • Author: Stanisław Gruszczyński x
Clear All Modify Search
Open access

Stanisław Gruszczyński

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.

Open access

Krzysztof Urbański and Stanisław Gruszczyński

Abstract

The article presents the results of attempts to use adaptive algorithms for classification tasks different soils units. The area of study was the Upper Silesian Industrial Region, which physiographic and soils parameters in the form of digitized was used in the calculation. The study used algorithms, self-organizing map (SOM) of Kohonen, and classifiers: deep neural network, and two types of decision trees: Distributed Random Forest and Gradient Boosting Machine. Especially distributed algorithm Random Forest (algorithm DRF) showed a very high degree of generalization capabilities in modeling complex diversity of soil. The obtained results indicate, that the digitization of topographic and thematic maps give you a fairly good basis for creating useful models of soil classification. However, the results also showed that it cannot be concluded that the best algorithm presented in this research can be regarded as a general principle of system design inference.