The Neural Method Of Sea Bottom Shape Modelling For The Spatial Maritime Information System
Price
Free (open access)
Transaction
Volume
51
Pages
9
Published
2000
Size
973 kb
Paper DOI
10.2495/PORTS000221
Copyright
WIT Press
Author(s)
A. Stateczny
Abstract
One of the basic problems of the spatial maritime information system is the three -dimensional numerical model of the sea bottom. It means depth interpolation of the sea bottom - in dependencies from geographical coordinates in any analysed point of an area. There exist a lot of classical methods of spatial modelling. In the article the neural method of marking depths in any investigated point of a reservoir and spatial modelling of the shape of the sea bottom is presented. Several types of neural networks were analysed and optimised to find a proper solution. Three of them, Multilayer Perception, Generalized Regression Neural Network (GRNN) and Radial Basis Function Network were chosen. The criterion of optimisation was accuracy of depth estimation. Trained neural networks can mark depth f
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