A novel three-direction datum transformation of geodetic coordinates for Egypt using artificial neural network approach
TL;DR: The results showed an accurate transforming datum using ANN technique for both common and check points, and the novel model improved the transformation coordinates by 37 to 72% in space directions.
Abstract: The geodetic datum transformation in-between local and global systems seen in the world are inspiring for the engineering applications. In this context, the Egyptian geodetic network has a limited observation for the terrestrial and satellite of the geodetic networks. Transforming the coordinates of the Egyptian datum, here we demonstrate the datum transformation in three directions from global to local coordinates that utilized the artificial neural network (ANN) technique as a new tool of datum transformation in Egypt. A conventional, which are the Helmert and Molodensky, and numerical, which are the regression, minimum curvature surface, and ANN, datum transformation techniques are investigated and compared over the available data in Egypt. The results showed an accurate transforming datum using ANN technique for both common and check points, and the novel model improved the transformation coordinates by 37 to 72% in space directions. A comparison between the conventional and numerical techniques shows that the accuracy of the developed ANN model is 20.16 cm in terms of standard deviation based on the residuals of the projected coordinates.
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TL;DR: In this paper, the authors used GPS/Levelling measurements of Kuwait and four heuristic regression methods including Least Square Support Vector Regression (LSSVR), GPR, Gaussian Process Regression, and LSSVR.
Abstract: This study investigates to use GPS/Levelling measurements of Kuwait and four heuristic regression methods including Least Square Support Vector Regression (LSSVR), Gaussian Process Regression (GPR)...
17 citations
01 Apr 2022
TL;DR: In this paper , the velocity vectors of GPS permanent stations in unknown locations are estimated by four methods: back propagation of artificial neural networks (BPANN), least square collocation (LSC), Bat algorithm (BA), and random forest algorithm (RFA).
Abstract: Installing permanent global positioning system (GPS) stations and receiving and monitoring long-term crustal deformation requires a high cost. Another solution, which could be an appropriate alternative, is applying some modern and smart estimation methods such as deep learning of artificial neural networks (DLANN). Based on the observations of the 42 GPS permanent stations in NW Iran, the velocity vectors of stations are estimated in unknown locations by four methods: back propagation of artificial neural networks (BPANN), least square collocation (LSC), Bat algorithm (BA) and random forest algorithm (RFA). BPANN has better performance and less variance than RFA, and the largest difference is in the gap areas, which estimates each vector method differently and it is preferred not to estimate these areas. The performances of LSC and BA algorithm is not better than BPANN. Therefore, it seems that the BPANN method can be considered as a suitable option for estimating the geodetic velocity field compared to other methods.
8 citations
TL;DR: In this article, the predictive capacity of three artificial neural network (ANN) models in predicting geodetic point velocities was explored, and the results showed that the GRNN model provided better accuracy than the MLPNN and RBFNN models.
Abstract: The prediction of an accurate geodetic point velocity has great importance in geosciences. The purpose of this work is to explore the predictive capacity of three artificial neural network (ANN) models in predicting geodetic point velocities. First, the multi-layer perceptron neural network (MLPNN) model was developed with two hidden layers. The generalized regression neural network (GRNN) model was then applied for the first time. Afterwards, the radial basis function neural network (RBFNN) model was trained and tested with the same data. Latitude (
$$\varphi$$
) and longitude (λ) were utilized as inputs and the geodetic point velocities (
$${V}_{X}$$
,
$${V}_{Y}$$
,
$${V}_{Z}$$
) as outputs to the MLPNN, GRNN, and RBFNN models. The performances of all ANN models were evaluated using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (
$${\text{R}}^{2}$$
). The first investigation demonstrated that it was possible to predict the geodetic point velocities by using all the components as output parameters simultaneously. The other result is that all ANN models were able to predict the geodetic point velocity with satisfactory accuracy; however, the GRNN model provided better accuracy than the MLPNN and RBFNN models. For example, the RMSE and MAE values were 1.77–1.88 mm and 1.44–1.51 mm, respectively, for the GRNN model.
7 citations
TL;DR: In this article, the authors investigated the coseismic displacement of 11 August 2012 with magnitudes 6.5 Mw and 6.3 Mw of Ahar-Varzegan earthquakes based on GPS observations and deep learning.
Abstract: The determination of crustal deformation can be measured by geodetic observations of permanent global positioning system (GPS) stations. In this study, the coseismic displacement of 11 August 2012 with magnitudes 6.5 Mw and 6.3 Mw of Ahar–Varzegan earthquakes has been investigated based on GPS observations and deep learning. For this purpose, data were processed at a 30-s rate of 13 Iran geodynamic stations with distances of 25 to 160 km from the earthquake epicenter and then were entered into deep learning. The results show that the horizontal displacement field of the Ahar–Varzegan earthquake has a mean value of 27.93 cm and 15.35 cm, which is estimated with the root mean square error (RMSE) of ±0.24 cm. Vertical displacement has been neglected due to the low accuracy of the z component and the low density of stations in the central seismic range. Also, the right lateral fault (cause of Ahar–Varzegan earthquake) to seismic displacement is evident; field observations and previous research confirm coseismic displacement values and right latera fault.
6 citations
TL;DR: It is found that the predictability accuracy of LSC increased when using soft computing techniques, and the LS-SVM integrated with LSC is recommended for enhanced predictability in geodetic applications.
Abstract: Abstract Least-squares collocation (LSC) is a crucial mathematical tool for solving many geodetic problems. It has the capability to adjust, filter, and predict unknown quantities that affect many geodetic applications. Hence, this study aims to enhance the predictability property of LSC through applying soft computing techniques in the stage of describing the covariance function. Soft computing techniques include the support vector machine (SVM), least-squares-support vector machine (LS-SVM), and artificial neural network (ANN). A real geodetic case study is used to predict a national geoid from the EGM2008 global geoid model in Egypt. A comparison study between parametric and soft computing techniques was performed to assess the LSC predictability accuracy. We found that the predictability accuracy increased when using soft computing techniques in the range of 10.2 %–27.7 % and 8.2 %–29.8 % based on the mean square error and the mean error terms, respectively, compared with the parametric models. The LS-SVM achieved the highest accuracy among the soft computing techniques. In addition, we found that the integration between the LS-SVM with LSC exhibits an accuracy of 20 % and 25 % higher than using LS-SVM independently as a predicting tool, based on the mean square error and mean error terms, respectively. Consequently, the LS-SVM integrated with LSC is recommended for enhanced predictability in geodetic applications.
6 citations
References
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Journal Article•
TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
Abstract: Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different "thinned" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
33,597 citations
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16 Jul 1998
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Abstract: From the Publisher:
This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks. Written in a concise and fluid manner, by a foremost engineering textbook author, to make the material more accessible, this book is ideal for professional engineers and graduate students entering this exciting field. Computer experiments, problems, worked examples, a bibliography, photographs, and illustrations reinforce key concepts.
29,130 citations
"A novel three-direction datum trans..." refers background or methods in this paper
...Although many different functions could be a successful transfer function, usually a differentiable and bounded function is used (Haykin 1994; Hagan et al. 1995)....
[...]
...The training process continues until the network error reaches an acceptable value or has a stable state of estimated unknown parameters (Haykin 1994; Hagan et al. 1995)....
[...]
...Three layers are the component of the ANN; these are input, hidden, and output layers; each layer contains one or more neurons (Ziggah et al. 2016; Haykin 1994; Hagan et al. 1995), as presented in Fig....
[...]
...Three layers are the component of the ANN; these are input, hidden, and output layers; each layer contains one or more neurons (Ziggah et al. 2016; Haykin 1994; Hagan et al. 1995), as presented in Fig....
[...]
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
18,794 citations
"A novel three-direction datum trans..." refers background or methods in this paper
...In this study, the standard multilayer feedforward network with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer is used because of its ability to approximate any measurable function to any desired degree of accuracy provided sufficiently many hidden units; in other word, it is a universal mapping tool (Hornik et al. 1989; Hagan et al. 1995)....
[...]
...…the hidden layer and a linear transfer function in the output layer is used because of its ability to approximate any measurable function to any desired degree of accuracy provided sufficiently many hidden units; in other word, it is a universal mapping tool (Hornik et al. 1989; Hagan et al. 1995)....
[...]
Book•
29 Dec 1995TL;DR: This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules, as well as methods for training them and their applications to practical problems.
Abstract: This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems. Features Extensive coverage of training methods for both feedforward networks (including multilayer and radial basis networks) and recurrent networks. In addition to conjugate gradient and Levenberg-Marquardt variations of the backpropagation algorithm, the text also covers Bayesian regularization and early stopping, which ensure the generalization ability of trained networks. Associative and competitive networks, including feature maps and learning vector quantization, are explained with simple building blocks. A chapter of practical training tips for function approximation, pattern recognition, clustering and prediction, along with five chapters presenting detailed real-world case studies. Detailed examples and numerous solved problems. Slides and comprehensive demonstration software can be downloaded from hagan.okstate.edu/nnd.html.
6,463 citations
Book•
01 Jun 1983
756 citations
"A novel three-direction datum trans..." refers background in this paper
...observations in radian units, and there is no correlation between them (Mikhail 1976)....
[...]
...…weights and biases as constants and geodetic coordinates (ϕ,λ) as independent observations in radian units, and there is no correlation between them (Mikhail 1976). σ2T̂ ¼ JΣxx J T ð16Þ where σ2 T̂ represents the variance of the estimated shift, Jis the Jacobian matrix relative to the geodetic…...
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