Regionalization of geographical space according to selected topographic factors in reference to spatial distribution of precipitation: application of artificial neural networks in GIS

Abstract

In this paper, the regionalization of geographical space according to selected topographic factors and the spatial distribution of precipitation is discussed. The model takes into account qualitative and quantitative data describing the conditions associated with the studied precipitation. In the modelling, data mining methods including data clustering methods for agglomeration and artificial neural networks for classification have been used. The reason for their use was the classification of the area due to conditions related to precipitation, the distinguishing of similar areas and the delimitation of the propagation of the phenomenon or transition zones. To realize the research aims, professional software for data management, spatial data analysis, mathematical calculations and data mining have been used. The result of the research was a model of the classes representing areas with specific conditions affecting the phenomenon, transition zones between classes and areas with conditions other than those in the surroundings of the measuring stations, which are not classified in any of the classes. Classification results indicate the boundaries of the areas in which we can model the values measured at stations, the transition zones of possible discontinuous change and areas in which the phenomenon should not be modelled due to significantly different conditions from those in the neighbourhoods of measuring stations. Unclassified areas are also potential locations for new measuring stations.

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