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Regional Mapping of the Geoid Using GNSS (GPS) Measurements and an Artificial Neural Network

Graduate Program in Geology, Remote Sensing and Digital Cartography Laboratory (LASERCA), Universidade do Vale do Rio dos Sinos (UNISINOS), Av. Unisinos, 950, CEP 93022-000 São Leopoldo, RS, Brazil
Laboratory of Geodetic Researches (LAGEO), Geosciences Institute, Geodetic Department, Universidade Federal do Rio Grande do Sul (UFRGS), Av. Bento Gonçalves, 9500, CEP 91501-970 Porto Alegre, RS, Brazil
Author to whom correspondence should be addressed.
Remote Sens. 2011, 3(4), 668-683;
Received: 22 December 2010 / Revised: 15 January 2011 / Accepted: 24 February 2011 / Published: 30 March 2011
(This article belongs to the Special Issue Global Positioning Systems (GPS) and Applications)
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The determination of the orthometric height from geometric leveling has practical difficulties that, despite a number of scientific and technological advances, passed a century without substantial modifications or advances. Currently, the Global Navigation Satellite System (GNSS) has been used with reasonable success for orthometric height determination. With a sufficient number of benchmarks with known horizontal and vertical coordinates, it is often possible to adjust using the least squares method mathematical expressions that allow interpolation of geoid heights. The objective of this study is to present an alternative method to interpolate geoid heights based on the technique of Artificial Neural Networks (ANNs). The study area is the Brazilian state of São Paulo, and for training the ANN the authors have used geoid height information from the EGM08 gravity model with a grid spacing of 10 minutes of arc. The efficiency of the model was tested at 157 points with known geoid heights distributed across the study area. The results were also compared with the Brazilian Geoid Model (MAPGEO2004). Based on those 157 benchmarks it was possible to verify that the model generated by ANNs provided a mean absolute error of 0.24 m in obtaining a geoid height value. Statistical tests have shown that there was no difference between the means from known geoid heights and geoid heights provided by the neural model for a significance level of 5%. It was also found that ANNs provided an improvement of 2.7 times in geoid height estimates when compared with the MAPGEO2004 geoid model. View Full-Text
Keywords: geoid height; earth gravitational model 2008; artificial neural networks geoid height; earth gravitational model 2008; artificial neural networks

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Veronez, M.R.; Florêncio de Souza, S.; Matsuoka, M.T.; Reinhardt, A.; Macedônio da Silva, R. Regional Mapping of the Geoid Using GNSS (GPS) Measurements and an Artificial Neural Network. Remote Sens. 2011, 3, 668-683.

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