DBPapers
DOI: 10.5593/SGEM2014/B21/S8.084

INTEGRATION NEURAL NETWORKS AND GIS IN MODELING LANDSCAPE CHANGES

V. Pechanec, J. Burian, Z. Dobesova
Wednesday 1 October 2014 by Libadmin2014

References: 14th International Multidisciplinary Scientific GeoConference SGEM 2014, www.sgem.org, SGEM2014 Conference Proceedings, ISBN 978-619-7105-10-0 / ISSN 1314-2704, June 19-25, 2014, Book 2, Vol. 1, 651-658 pp

ABSTRACT
Geographical information system is very powerful tool to manage and analyses land use data. The integration of Geographic Information Systems and Artificial Neural Networks offers a mechanism to lower the cost of analysis of landscape change by reducing the amount of time spent interpreting data. Artificial Neural Networks (ANNs) have been proven to be useful in the interpretation of natural resource information. Back-Propagation Neural Networks are one of the most common and widely used architectures. Many architectures and types of ANNs have been developed, and many of them are PC-based. Change prediction is based on the analysis of the Markov chain. This process determines the condition of the system on the basis of its previous condition and likelihood of changes which have occurred between them. Models of change serve as useful tools for exploring the various mechanisms by which land use change occurs actual projecting and potential future environmental and evaluating the impact.

Keywords: Land use change, Artificial neural networks, Geographic information systems

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