Utilization of geographically weighted regression for geoid modelling in Egypt

Dawod, Gomaa M.; Abdel-Aziz, Tarek M.

doi:10.1515/jag-2019-0009

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…Through the years many interpolation methods have been involved in generating conversion surfaces (in local and regional scales) enabling transition between geometric and physical heights, e.g., polynomial regression (Borowski and Banaś, 2019;Gucek and Bašić, 2009;Kim et al, 2018;Zhong, 1997), neural networks (Aky-ilmaz et al, 2009;Kaloop et al, 2021), geographically weighted regression (Dawod and Abdel-Aziz, 2020), kriging (Ligas and Szombara, 2018;Orejuela et al, 2021), least-squares collocation (LSC) (You, 2006), Inverse Distance Weighting (IDW) (Radanović and Bašić, 2018) to mention only a few. Very often, conversion surfaces take the form of corrector surfaces due to the use of global geopotential models or gravimetric models generated prior to eliminating inconsistencies by fitting to GNSS/levelling data (Elshambaky, 2018;You, 2006).…”

Section: Introductionmentioning

confidence: 99%

A crossvalidation-based comparison of kriging and IDW in local GNSS/levelling quasigeoid modelling

Ligas

Lucki

Banasik

2022

Reports on Geodesy and Geoinformatics

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This study compares two interpolation methods in the problem of a local GNSS/levelling (quasi) geoid modelling. It uses raw data, no global geopotential model is involved. The methods differ as to the complexity of modelling procedure and theoretical background, they are ordinary kriging/least-squares collocation with constant trend and inverse distance weighting (IDW). The comparison itself was done through leave-one-out and random (Monte Carlo) cross-validation. Ordinary kriging and IDW performance was tested with a local (using limited number of data) and global (using all available data) neighbourhoods using various planar covariance function models in case of kriging and various exponents (power parameter) in case of IDW. For the study area both methods assure an overall accuracy level, measured by mean absolute error, root mean square error and median absolute error, of less than 1 cm. Although the method of IDW is much simpler, a suitably selected parameters (also trend removal) may contribute to differences between methods that are virtually negligible (fraction of a millimetre).

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Section: Introductionmentioning

confidence: 99%

A crossvalidation-based comparison of kriging and IDW in local GNSS/levelling quasigeoid modelling

Ligas

Lucki

Banasik

2022

Reports on Geodesy and Geoinformatics

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“…The gravimetric method offers benefits for areas with a homogeneous coverage of terrestrial data, but it is involving mathematical and computational procedures (Featherstone et al, 1998). The geometric approach has been widely used for a relatively small area, which interpolates geoid heights based on the GPS-derived heights and leveled heights at some points (Zhong, 1997;Erol & Çelik, 2004;Rabah & Kaloop, 2013;Ligas & Kulczycki, 2018;Dawod & Abdel-Aziz, 2020). The global geoid models like EGM96 can achieve the accuracy of regional or local geoid models by this method.…”

Section: Methodsmentioning

confidence: 99%

Vertical accuracy evaluation free access digital elevation models (DEMs): case Fergana Valley in Uzbekistan

Fazilova,

Arabov

2023

Earth sci. res. j.

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In this study, the vertical accuracy of the Shuttle Radar Topography Mission Digital Elevation Model Version 2.0 (SRTM30), the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM Version 2.0 (ASTER GDEM2), and Advanced Land Observing Satellite World 3D Digital Surface Model Version 2.1 (ALOS AW3D30) was statistically assessed using GPS data. The Fergana Valley area was chosen as a study region, where the land surface can reflect tectonic processes. The values of ellipsoidal heights of 27 points of the regional GPS network were chosen as reference data. The geometric approach using GPS/leveling data and EGM96 global geopotential model-based geoid undulations was applied for geoid surface fitting. The geoid height corrections range ranged from –0.66 m to 0.87 m. Root-Mean-Square errors of ~10.0 m, ~16.4 m, and ~6.6 m was obtained for SRTM30, ASTER GDEM2, and ALOS AW3D30, respectively. It was found that compared with the reference model, all the global DEMs in mountainous areas generally overestimated elevation and the value of vertical accuracy at a 90% confidence level by 3-6 meters exceeded the declared by distributors. But ALOS AW3D30 proved to be the most accurate DEM that best represents the topography of the earth’s surface and could be used for some engineering applications in Fergana Valley.

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“…Two different methods are used in geoid modeling: gravimetric and geometric (Featherstone et al, 1998;Kotsakis & Sideris, 1999). With the geometric approach, many different techniques such as interpolation and leastsquares collocation (LSC) methods are used to determine a local geoid (Zhong, 1997;Zhan-ji & Yong-qi, 1999;Yanalak & Baykal, 2001;Erol & Çelik, 2004;Zaletnyik et al, 2004;Erol et al, 2008;Tusat, 2011;Rabah & Kaloop, 2013;Doganalp, 2016;Das et al, 2018;Ligas & Kulczycki, 2018;Tusat & Mikailsoy, 2018;Dawod & Abdel-Aziz, 2020). For example, Doganalp & Selvi (2015), using GNSS/leveling data (40 reference points and 205 test points), determined a geoid of the 57-km-long Nurdagi-Gaziantep highway project.…”

Section: Introductionmentioning

confidence: 99%

Geoid undulation prediction using ANNs (RBFNN and GRNN), multiple linear regression (MLR), and interpolation methods: A comparative study

Konakoǧlu

Akar

2022

Earth sci. res. j.

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The present work aimed to develop a prediction model to estimate geoid undulation and to compare its efficiency with other methods including radial basis function neural network (RBFNN), generalized regression neural network (GRNN), multiple linear regression (MLR) and, ten different interpolation methods. In this study, the k-fold cross-validation method was used to evaluate the model and its behavior on the independent dataset. With this validation method, each of a k number of groups has the chance to be divided into training and testing data. The performances of the methods were evaluated in terms of the root mean square error (RMSE) mean absolute error (MAE), Nash–Sutcliffe efficiency coefficient (NSE), and correlation coefficient (R2) and using graphical indicators. The evaluation of the performance of the datasets obtained using cross-validation was done in two ways. If we accept the method having the minimum error result as the most appropriate method, the natural neighbor (NN) method in the DS#5 dataset gave better results than the other methods (RMSE=0.14173 m, MAE=0.09729 m, NSE=0.98986, and R2=0.99011. On the other hand, it has been observed that, the GRNN method exhibited the best performance, on average, with RMSE=0.18539 m, MAE=0.13676 m, NSE=0.98229, and R2=0.98249.

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Utilization of geographically weighted regression for geoid modelling in Egypt

Cited by 9 publications

References 18 publications

A crossvalidation-based comparison of kriging and IDW in local GNSS/levelling quasigeoid modelling

A crossvalidation-based comparison of kriging and IDW in local GNSS/levelling quasigeoid modelling

Vertical accuracy evaluation free access digital elevation models (DEMs): case Fergana Valley in Uzbekistan

Geoid undulation prediction using ANNs (RBFNN and GRNN), multiple linear regression (MLR), and interpolation methods: A comparative study

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