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H. T. Elshambaky

Bio: H. T. Elshambaky is an academic researcher. The author has contributed to research in topics: Local coordinates & Geodetic datum. The author has an hindex of 1, co-authored 1 publications receiving 7 citations.

Papers
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Journal ArticleDOI
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.

11 citations


Cited by
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Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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