Application of different radial basis function networks in the illegal waste dump-surface modelling

In quality assessment of digital elevation models (DEMs), geodetic field measurements have an important, but also a limited, role. They can achieve high accuracy, but the acquisition of data is time consuming, expensive and, in areas with a high-resolution DEM accessibility, non-effective. Therefore...

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Bibliographic Details
Published in:Central European journal of operations research 2019-09, Vol.27 (3), p.783-795
Main Authors: Pavlovčič-Prešeren, Polona, Stopar, Bojan, Sterle, Oskar
Format: Article
Language:English
Subjects:
Basis functions
Business and Management
Computer simulation
Digital Elevation Models
Geodesy
Knowledge acquisition
Knowledge management
Landfills
Landscape
Modelling
Neural networks
Operations research
Operations Research/Decision Theory
Original Paper
Quality assessment
Radial basis function
Refuse and refuse disposal
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Summary:In quality assessment of digital elevation models (DEMs), geodetic field measurements have an important, but also a limited, role. They can achieve high accuracy, but the acquisition of data is time consuming, expensive and, in areas with a high-resolution DEM accessibility, non-effective. Therefore, field measurements are only performed at discrete selected points to evaluate the quality of the DEM used for further studies. The aim of this article is to show that differences in heights from field measurements and from a DEM can be used for height-deviation-surface modelling and for possible improvements in the available DEM. The procedure is related to situations where significant changes in the landscape occur. For surface modelling, this research included several radial basis function networks (RBFNs). From simulations, knowledge was acquired of appropriate results based on varying amounts of input data as well as on different neural network activation functions. This study indicates the potential use of geodetic field measurements in the improvement of a local DEM by RBFNs.
ISSN:1435-246X
1613-9178
DOI:10.1007/s10100-018-0586-z