Application of a Back-Propagation Artificial Neural Network to Regional Grid-Based Geoid Model Generation Using GPS and Leveling Data

Lin, Lao-Sheng

doi:10.1061/(asce)0733-9453(2007)133:2(81)

Cited by 25 publications

(13 citation statements)

References 7 publications

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“…The test results indicated that the accuracy of the estimated geometric undulation interpolation that used the LS-SVM was in the order of 1.5 cm. Based on these results, the estimated undulation accuracy attained using the LS-SVM was superior to that attained using the curve fitting and BPANN methods (LIN, 2007) by an order of 2-4 cm in the same test area.…”

Section: Study Results and Analysismentioning

confidence: 86%

“…Numerous researchers (ABDALLA et al, 2011, KAO et al, 2011, LIN, 2007, You, 2006 have investigated combined methods for improving local geoids, using GPS and geodetic leveling data. These scholars have presented multiple useful tools and interpolation methods.…”

Section: The Least Squares Support Vector Machinementioning

confidence: 99%

“…In brief, the machine must minimize the upper bound of the error probability; thus, the SVM is the realization of this theory. The SVM is simpler compared with traditional artificial neural networks (ANN; ACKIN and CELIK,2013;LIN, 2007); however, the generalization abilities of numerous approaches employing the statistical decision method exhibit difficulties deriving the desired results when using limited samples. Thus, Vapnik(1995aVapnik( ,1995b proposed the Vapnik-Chervnenkis dimension (VC dimension) as follows: a function separates N samples into n 2 forms and the VC dimension can divide the largest number of samples (N).…”

Section: The Least Squares Support Vector Machinementioning

confidence: 99%

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Using a least squares support vector machine to estimate a local geometric geoid model

et al. 2014

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In this study, test-region global positioning system (GPS) control points exhibiting known first-order orthometric heights were employed to obtain the points of plane coordinates and ellipsoidal heights by using the real-time GPS kinematic measurement method. Plane-fitting, second-order curve-surface fitting, backpropagation (BP) neural networks, and least-squares support vector machine (LS-SVM) calculation methods were employed. The study includes a discussion on data integrity and localization, changing reference-point quantities and distributions to obtain an optimal solution. Furthermore, the LS-SVM was combined with local geoidal-undulation models that were established by researching and analyzing3 kernel functions. The results indicated that the overall precision of the local geometric geoidal-undulation values calculated using the radial basis function (RBF) and third-order polynomial kernel function was optimal and the root mean square error (RMSE) was approximately ± 1.5 cm. These findings demonstrated that the LS-SVM provides a rapid and practical method for determining orthometric heights and should serve as a valuable academic reference regarding local geoid models. RESUMO Este estudo empregou pontos de controle de GPS em regiões-teste com altitudes ortométricas de primeira ordem previamente conhecidas, para a obtenção de pontos de coordenadas planas e altitudes elipsoidais, com a utilização do método cinemático em tempo real (RTK). Este estudo aplicou o ajuste de superfície de primeira ordem, ajuste de superfície de segunda ordem, método da retropropagação para redes neurais e "support vector machine" por mínimos quadrados (LS-SVM). Integridade e localização de dados foram examinados, e a certa quantidade e distribuição de pontos de referência modificados para obtenção da solução ideal. O estudo contou também com o LS-SVM e com o modelo de altitude geoidal local que foi estabelecido usando 3 funções de Kernel para análises e pesquisas. Os resultados indicam que a precisão total dos valores calculados das ondulações geóidais geométricas locais usando a rede neural artificial e o polinômio de terceiraordem da função de Kernel foram ideais com o valor quadrático médio de aproximadamente ± 1.5 cm. O resultado mostrou que o SVM oferece o método rápido e prático para obtenção de altitudes ortométricas e provê a pesquisa acadêmica de referência para modelos geoidais locais. Palavras-chave: Máquina de Vetor Suporte para Mínimos Quadrados; Função de Kernel; Modelo de Geóide Local. INTRODUCTIONThe gravimetric method is the technique most commonly used to precisely determine the local geoid. Recently, a gravimetric geoid model covering Taiwan was generated using gravity survey data, which are relatively difficult and timeconsuming to measure and often yield results that do not fit well with the local terrain. Gravimetric geoid results fit large rather than small areas. Because of the lack of gravity data in mountainous regions of Taiwan, this study was conducted to generate a regional geoid model that ...

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Section: Study Results and Analysismentioning

confidence: 86%

Section: The Least Squares Support Vector Machinementioning

confidence: 99%

Section: The Least Squares Support Vector Machinementioning

confidence: 99%

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Using a least squares support vector machine to estimate a local geometric geoid model

et al. 2014

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“…Numerous studies have investigated the development of suitable local geoid undulation models by applying various fitting methods to measurements of ellipsoidal height h and leveling orthometric height H obtained using global positioning systems (GPSs) (Odera et al, 2014;Abdalla et al, 2011;Abdalla and Fairhead, 2011;Akcin and Celik, 2013;Abdalla, 2009;Featherstone et al, 2004;Kao et al, 2014 ;Kao, 2006;Kotsakis and Sideris, 1999;Kao and Bethel, 1992(a); Kao and Bethel, 1992(b) ;Ning, 2015;Lin, 2007;Tranes et al, 2007;Kiamehr, 2006;Ustun and Demirel, 2006;You, 2006;Yang and Chen, 1999). The geoid of each position in a location can be calculated to determine H for each position.…”

Section: Introductionmentioning

confidence: 99%

Using Particle Swarm Optimization to Establish a Local Geometric Geoid Model

et al. 2017

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Abstract:There exist a number of methods for approximating the local geoid surface and studies carried out to determine a local geoid. In this study, performance of geoid by PSO method in modeling local geoid was presented and analyzed. The ellipsoidal heights (h), derived from GPS observations, and known orthometric heights from first-order bench marks were first used to create local geometric geoid model, then the PSO method was used to convert ellipsoidal heights into orthometric heights (H). The resulting values were used to compare between the spirit leveling and GPS methods. The adopted PSO method can improve the fitting of local geometric geoid by quadratic surface fitting method, which agrees with the known orthometric heights within ±1.02cmthe Cartography produced: General Map, Partial Maps, Profile, Cross Sections and others. Keywords: Particle swarm optimization (PSO); Quadratic surface fitting; Ellipsoidal height; Orthometric height. Resumo:Existe uma série de métodos para aproximar a superfície do geoide local bem como vários estudos conduzidos para determinar um geoide local. Neste estudo, apresentou-se e analisou-se o desempenho do geoide pelo método de otimização por exame de partículas (PSO, do inglês Particle Swarm Optimization) na modelagem do geoide local. As altitudes elipsoidais (h), derivada de observações GPS, e altitudes ortométricas de referências de nível de primeira ordem foram usados para criar um modelo do geoide geométrica local, na sequencia usou-se o método PSO para converter altitudes elipsoidais em altitudes ortométricas (H

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“…Concerning application of soft computing technique Kavzoglu and Saka 2005 [6] and Lao-Sheng Lin 2006 [7] employed artificial neural network (ANN) for approximating the GPS/levelling geoid instead of the corrector surface itself. Both of them applied feed-forward ANN with the standard sigmoid activation functions and different number of hidden layers.…”

Section: Introductionmentioning

confidence: 99%

Modelling local GPS/levelling geoid undulations using Support Vector Machines

Zaletnyik¹,

Völgyesi²,

Paláncz³

2008

Per. Pol. Civil Eng.

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Support vector machines (SVM) with wavelet kernel has been applied to the correcting gravimetric geoid using GPS/levelling data. These data were divided into a training and a validation set in order to ensure the extendability of the approximation of the corrector surface. The optimal parameters of the SVM were considered as a trade-off between accuracy and extendability of the solution in order to avoid overlearning. Employing 194 training points and 110 validation points, SVM provided an approximation with less than 3 cm standard deviation of the error and nearly perfect extendability.

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Application of a Back-Propagation Artificial Neural Network to Regional Grid-Based Geoid Model Generation Using GPS and Leveling Data

Cited by 25 publications

References 7 publications

Using a least squares support vector machine to estimate a local geometric geoid model

Using a least squares support vector machine to estimate a local geometric geoid model

Using Particle Swarm Optimization to Establish a Local Geometric Geoid Model

Modelling local GPS/levelling geoid undulations using Support Vector Machines

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