Study on the Applications of Neural Networks for Processing Deformation Monitoring Data

Gao, Cai Yun; Cui, Xi Min; Hong, Xue Qian

doi:10.4028/www.scientific.net/amm.501-504.2149

Cited by 6 publications

(2 citation statements)

References 3 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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“…In recent years, artificial neural networks (ANNs) have gained popularity in geodetic sciences community. ANNs have been used for coordinate transformation (Zaletnyik, 2004;Maria, 2012;Tierra, et al, 2008;Gullu, 2010;Weiwei and Xiudong, 2010;Gullu et al, 2011;Tierra and Romero, 2014;Ziggah et al, 2016), geoid determination (Kavzoglu and Saka, 2005;Pikridas, et al, 2011;Memarian Sorkhabi, 2015), geodetic deformation modelling (Bao et al, 2011;Chen and Zeng, 2013;Du et al, 2014;Gao et al, 2014), image and signal processing (Ibrahim, 2010;ALAllaf, 2012;Hai and Thuy, 2012) and determination of earth orientation parameters (Schuh, 2002;Liao, 2012). In this study, the feed forward back propagation (FFBP), cascade forward back propagation (CFBP) and radial basis function neural network (RBFNN) were used as alternative coordinate transformation methods to conduct 2D transformation between the ED50 and ITRF96 systems.…”

Section: Introductionmentioning

confidence: 99%

2d Coordinate Transformation Using Artificial Neural Networks

Konakoǧlu¹,

Çakir²,

Gökalp³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Two coordinate systems used in Turkey, namely the ED50 (European Datum 1950) and ITRF96 (International Terrestrial Reference Frame 1996) coordinate systems. In most cases, it is necessary to conduct transformation from one coordinate system to another. The artificial neural network (ANN) is a new method for coordinate transformation. One of the biggest advantages of the ANN is that it can determine the relationship between two coordinate systems without a mathematical model. The aim of this study was to investigate the performances of three different ANN models (Feed Forward Back Propagation (FFBP), Cascade Forward Back Propagation (CFBP) and Radial Basis Function Neural Network (RBFNN)) with regard to 2D coordinate transformation. To do this, three data sets were used for the same study area, the city of Trabzon. The coordinates of data sets were measured in the ED50 and ITRF96 coordinate systems by using RTK-GPS technique. Performance of each transformation method was investigated by using the coordinate differences between the known and estimated coordinates. The results showed that the ANN algorithms can be used for 2D coordinate transformation in cases where optimum model parameters are selected.

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Performance evaluation of artificial neural networks for planimetric coordinate transformation—a case study, Ghana

Ziggah

Tierra

et al. 2016

Arab J Geosci

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Study on the Applications of Neural Networks for Processing Deformation Monitoring Data

Cited by 6 publications

References 3 publications

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

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

2d Coordinate Transformation Using Artificial Neural Networks

Performance evaluation of artificial neural networks for planimetric coordinate transformation—a case study, Ghana

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