Polynomials, radial basis functions and multilayer perceptron neural network methods in local geoid determination with GPS/levelling

Çakir, Leyla; Yılmaz, Nazan

doi:10.1016/j.measurement.2014.08.003

Cited by 32 publications

(18 citation statements)

References 26 publications

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“…Such models include simple polynomials (Zhong, 1997), radial basis functions, multilayer perception neural network methods (Cakir and Yilmaz, 2014) etc. A back propagation artificial neural network was applied to develop a regional gridbased geoid model using GPS derived ellipsoidal heights and the orthometric heights (Lin, 2007 are simple, easy to understand and adoption of these models for development of geoid surface has been widely reported and therefore considered in the present study.…”

Section: Introductionmentioning

confidence: 99%

Polynomial interpolation methods in development of local geoid model

Das

Samanta

Jana

et al. 2018

The Egyptian Journal of Remote Sensing and Space Science

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a b s t r a c tIn geodesy three surfaces, the physical surface of the earth, the geoid and the reference ellipsoid are encountered giving rise to orthometric height (h), the ellipsoidal height (H) and the geoidal separation (N). The orthometric height and the ellipsoidal height are with reference to the geoid and the reference ellipsoid respectively. The vertical separation between the ellipsoid and the geoid is the geoidal separation. A mathematical relation depicting the surface of the geoid with regard to the reference ellipsoid is the geoid model. It relates the geoidal separation with the horizontal location.The Global Navigational Satellite System provides precise location of points on the surface of the earth. The vertical location provided is the ellipsoidal height which needs conversion to a more usable format, the orthometric height. This is done by integrating ellipsoidal heights with a geoid model. The accuracy of conversion depends on the accuracy of geoid model. Therefore, development of geoid model has become a current area of research in geodesy.Objective of this study is to develop a local geoid model by employing various polynomial models and thereafter to analyse the accuracy of these models. The test area is in Papua New Guinea. The geometric method is used for computation of the geoidal separation from ellipsoidal and orthometric heights and thereafter the horizontal coordinates and the geoidal separation are used to develop the geoid surface using second, third and fourth degree polynomials. The study shows that the third degree polynomial provided an accuracy of ±20 cm. Ó 2017 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).

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Section: Introductionmentioning

confidence: 99%

Polynomial interpolation methods in development of local geoid model

Das

Samanta

Jana

et al. 2018

The Egyptian Journal of Remote Sensing and Space Science

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“…A neural network model with single hidden layer containing sufficiently neuron nodes can be used to approximate any continuous function with any precision. However, using a second hidden layer may give better results for some specific problems [23]. Based on the complexity of the relationship between input and output during the hot air forced convective thin layer drying of the sewage sludge, the BP neural network models with two hidden layers were developed to predict the moisture content and the average temperature of the sludge thin layer.…”

Section: Back-propagation (Bp) Neural Network Modelsmentioning

confidence: 99%

Artificial neural network modeling of thin layer drying behavior of municipal sewage sludge

Huang

Chen

2015

Measurement

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“…As it is a supervised-learning ANN, there must be both input and target data so the MLP can be correctly trained and optimized [22]. Since the estimation mechanism of ANNs is based on non-linear interpolation, they strongly depend on the range of data covered by the database, because if the model were forced to extrapolate, the error attained in this case would increase [16,23]. Additionally, they highly depend on the statistical quality of the data available (in terms of low deviation and good signal-to-noise ratio) to perform accurate estimations [23].…”

Section: Artificial Neural Networkmentioning

confidence: 99%