Titlebook: GeoComputational Modelling; Techniques and Appli Manfred M. Fischer,Yee Leung Book 2001 Springer-Verlag Berlin Heidelberg 2001 Computer.Geo

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https://doi.org/10.1007/978-3-319-59069-1ral network applications in this area is relatively new, dating back only about six to seven years. The first studies established the feasibility of error-based learning systems such as backpropagation (see Key, Maslanik and Schweiger 1989; McClellan et al. 1989; Benediktsson, Swain and Ersoy 1990;

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Research Methodology and Data Basis,e has been the development of spatial interaction models. Spatial interaction can be broadly defined as movement of people, commodities, capital and/or information over geographical space (see Batten and Boyce 1986). Such interaction encompasses such diverse behaviour as migration, travel-to-work, s

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1430-9602 ooms.Establishes general principles and builds solid foundatGeocomputation may be viewed as the application of a computational science paradigm to study a wide range of problems in geographical systems contexts..This volume presents a clear, comprehensive and thoroughly state-of-the-art overview of

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International Environmental Policy, [GIS]. But GeoComputation is not GIS. There does exist a relationship with GIS, defined as geographical information science, but GeoComputation has other relationships too that are just as important, for example, with computer science, pattern recognition and statistics, neurocomputing and computational intelligence technologies.

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Mohd Ismail Abd Aziz,Doria Abdullahl systems becoming more and more complex and highly fluid, non-linearity prevails and evolution is full of surprises. It is essential to develop approaches which can effectively analyse complexity, non-linearity and dynamics in spatial systems in general, and in particular spatial classification and knowledge acquisition.

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GeoComputational Modelling — Techniques and Applications: Prologue [GIS]. But GeoComputation is not GIS. There does exist a relationship with GIS, defined as geographical information science, but GeoComputation has other relationships too that are just as important, for example, with computer science, pattern recognition and statistics, neurocomputing and computational intelligence technologies.

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Neural and Evolutionary Computation Methods for Spatial Classification and Knowledge Acquisitionl systems becoming more and more complex and highly fluid, non-linearity prevails and evolution is full of surprises. It is essential to develop approaches which can effectively analyse complexity, non-linearity and dynamics in spatial systems in general, and in particular spatial classification and knowledge acquisition.