GPS-derived Geoid using Artificial Neural Network and Least Squares Collocation

Stopar, Bojan; Ambrožič, Tomaž; Kuhar, Miran; Turk, Goran

doi:10.1179/003962606781483340

Cited by 12 publications

(17 citation statements)

References 8 publications

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“…This assertion is well documented in literature. For example, ANN has been applied to solve most coordinate transformation problems between global and local datums (Gullu 2010;Gullu et al 2011;Lin and Wang 2006;Mihalache 2012;Tierra et al 2008Tierra et al , 2009Tierra and Romero 2014;Turgut 2010;Yilmaz and Gullu 2012;Zaletnyik 2004), for GPS height conversion (Fu and Liu 2014;Liu et al 2011;Lei and Qi 2010;Tieding et al 2010;Wu et al 2012a), in geodetic deformation modelling (Bao et al 2011;Du et al 2014a, b;Gao et al 2014;Pantazis and Eleni-Georgia 2013;Yilmaz and Gullu 2014;Yilmaz 2013), earth orientation parameters determination (Liao et al 2012;Schuh et al 2002;Yu et al 2015), precise orbital prediction (He-Sheng 2006;Li et al 2014), gravity anomaly estimation (Hajian et al 2011;Hamid and Mohammad 2013;Tierra and De Freitas 2005), geoid determination (Kavzoglu and Saka 2005;Pikridas et al 2011;Stopar et al 2006;Sorkhabi 2015;Veronez et al 2006Veronez et al , 2011, transforming from cartesian coordinates to geodetic coordinates (Civicioglu 2012) and many others.…”

Section: Introductionmentioning

confidence: 98%

Capability of Artificial Neural Network for Forward Conversion of Geodetic Coordinates $$(\phi ,\lambda ,h)$$ ( ϕ , λ , h ) to Cartesian Coordinates (X, Y, Z)

Ziggah

et al. 2016

Math Geosci

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The standard forward transformation equation plays a major role in coordinate transformation between global and local datums. Thus, it is a prerequisite step in the forward conversion of geodetic coordinates into cartesian coordinates in coordinate transformation from global to local datum and vice versa. Numerous studies have been carried out on converting cartesian coordinates to geodetic coordinates (reverse procedure) through the application of iterative, approximate, closed form, vector-based and computational intelligence algorithms. However, based on literature covered pertaining to this study, it was realized that the existing researches do not fully address the issue of applying and testing alternative techniques in the case of the forward conversion. Hence, the purpose of this present study was to explore the coordinate conversion performance of two different artificial neural network approaches (backpropagation artificial neural network (BPANN) and radial basis function neural network (RBFNN)) and multiple linear regression (MLR). The statistical findings revealed that the BPANN, RBFNN and MLR offered satisfactory prediction of cartesian coordinates. However, the RBFNN compared to BPANN and MLR showed better stability and more accurate prediction results. Furthermore, in terms of maximum three-dimensional position error, the RBFNN attained 0.004 m while 0.011 and 0.627 m were achieved, respectively, by MLR and BPANN. By virtue of the success achieved in this study, the main conclusion drawn here is that RBFNN provides a promising alter-B Hu Youjian 123 Math Geosci native in the forward conversion of geodetic coordinates into cartesian coordinates. Therefore, the capability of artificial neural network as a powerful tool for solving majority of function approximation problems in geodesy has been demonstrated.

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Section: Introductionmentioning

confidence: 98%

Capability of Artificial Neural Network for Forward Conversion of Geodetic Coordinates $$(\phi ,\lambda ,h)$$ ( ϕ , λ , h ) to Cartesian Coordinates (X, Y, Z)

Ziggah

et al. 2016

Math Geosci

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“…It has been a method for local geoid determinations. A number of studies carried out to determine a local geoid (KAVZAOGLU and SAKA, 2005;STOPAR, et al, 2006;VERONEZ, et al, 2006;GULLU, et al, 2011). However, the geoid determined has not been compared with the one approximated by Kriging method.…”

Section: Introductionmentioning

confidence: 99%

Performance of artificial neural networks on kriging method in modeling local geoid

Akçın

Çelik

2013

Bol. Ciênc. Geod.

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Transformation of ellipsoidal heights determined by satellite techniques into local leveling heights requires geoid heights at points of interest. However, the geoid heights at each point are not available. In order to determine them, the local geoid in the transformation area must be modeled or computed by an appropriate method, one way of doing it, is to use control points both of whose ellipsoidal and local leveling heights are available. In this study, performance of geoid by ANN compared to Kriging method in modeling local geoid was presented. Moreover, the transformation ability of the methods was investigated through a geodetic test network in Bursa Metropolitan Area of Turkey. The results suggest that the model by ANN exhibit better results than the one by Kriging Method. Keywords: Geoid; Local Geoid Modeling; ANN; Kriging; Transformations. RESUMOA transformação das altitudes elipsoidais determinadas por técnicas satelitais em locais de altitudes niveladas requer o conhecimento da altitude geoidal no ponto de interesse. Entretanto, as altitudes geoidais de cada ponto nem sempre estão disponíveis. A fim de determiná-las, um geóide local de transformação deve ser determinado ou calculado através de métodos apropriados. Uma maneira de fazer isto, é usar pontos de controle de ambos cujas altitudes elipsoidais e niveladas estejam disponíveis. Neste estudo, apresenta-se os resultados da comparação entre o geóide obtido por ANN e o método da Krigagem na modelagem do geóide local. Akcin,H. ; Celik, C. T. Bol. Ciênc. Geod., sec. Artigos, Curitiba, v. 19, n o 1, p.84-97, jan-mar, 2013. 8 5 Além disso, a habilidade da transformação dos métodos foi investigada através de uma rede geodésica teste na área metropolitana de Bursa na Turquia. Os resultados sugerem que o modelo por ANN exibe melhores resultados que o método da Krigagem. Palavras-chave: Geóide; Modelamento do geóide local; ANN; Krigagem; Transformações.

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“…Kavzaoğlu and Saka (2005) or as part of these classical statistical techniques, e.g. Stopar et al (2006)), adaptive network-based fuzzy inference systems (ANFIS) (Yılmaz and Arslan, 2008) and wavelet neural networks (Erol, 2007) were also evaluated by researchers in the most recent investigations on local geoid modelling.…”

Section: Local Gnss/levelling Geoidsmentioning

confidence: 99%