Global navigation satellite system (GNSS): a utility for sustainable development in Ghana

Fosu, Collins; Poku-Gyamfi, Yaw; Hein, Guenter W.

doi:10.4314/just.v27i2.33049

Cited by 5 publications

(5 citation statements)

References 1 publication

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“…The Golden Triangle is made up of three permanent stations having 100 km radius of coverage situated at the vertices of the three largest cities (Sekondi-Takoradi, Kumasi and Accra) where almost all the natural resources like gold, oil, timber, cocoa, diamond, manganese, bauxite among others are located in Ghana (Poku-Gyamfi, 2009). These three permanent reference stations form a triangle of sides 200 km that cover a total area of 79857 km 2 representing 33.5% of the total land mass (238540 km 2 ) of Ghana (Fosu et al, 2006).…”

Section: Study Area and Data Descriptionmentioning

confidence: 99%

A 3D Procrustean Approach to Transform WGS84 Coordinates to Ghana War Office 1926 Reference Datum

Kumi-Boateng¹,

Ziggah²

2020

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Abstract Satellite positioning technique such as Global Positioning System (GPS) is available to all countries for geospatial positioning. The availability of such positioning technique has revolutionised surveying in Ghana. The GPS operates on a global reference frame to fix control points for surveying and mapping purposes. There is therefore the need to transform coordinates from the satellite-based datum to the Ghana War Office 1926 datum. Several iterative methods have been proposed over the years for coordinate transformation and have been found to exhibit good transformation accuracy. However, these iterative methods always demand the linearisation of the transformation model equations and initial approximation values of the yet to be determined transformation parameters. These computational processes further enhance the computational complexity of the iterative methods and longer convergence time. As alternative solution, the Procrustes method has been proposed and applied to solve coordinate transformation problems in different geodetic reference networks. Review of previous studies indicates that the Procrustes method is direct, simple to use and produce satisfactory transformation accuracy. This method, however, is yet to be applied to ascertain its efficiency in the Ghana geodetic reference network. Therefore, this study utilised the 3D Procrustean approach to transform coordinates from World Geodetic System 1984 (WGS84) to Ghana War Office 1926 reference datum. The technique produced Root Mean Square Horizontal Error (RMSHE), Arithmetic Mean of the Horizontal Error (AMHE) and Standard Deviation (SD) values of 1.003 m, 0.901 m and 0.452 m, respectively. This study is serving as an extension to the ongoing research works to determine optimal transformation model for Ghana geodetic reference network. Keywords: Procrustean Approach, Coordinate Transformation, Conformal Model, Satellite Positioning

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Section: Study Area and Data Descriptionmentioning

confidence: 99%

A 3D Procrustean Approach to Transform WGS84 Coordinates to Ghana War Office 1926 Reference Datum

Kumi-Boateng¹,

Ziggah²

2020

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“…It shares borders with Togo, Ivory Coast, and Burkina Faso in the East, West and North directions respectively, and it is bordered with the Gulf of Guinea in the southernmost direction. Ghana has a total area coverage of approximately 238 540 km 2 and bounded between latitudes 4° 30' N and 11° N, and longitudes 3° W and 1° E (Fosu et al, 2006;Mugnier, 2000). The Ghana geodetic reference network popularly known as the golden triangle which covers the South most part of the country was used as the case study area (Figure 1).…”

Section: Brief Description Of the Study Areamentioning

confidence: 99%

Feasibility of Using Group Method of Data Handling (Gmdh) Approach for Horizontal Coordinate Transformation

Kumi-Boateng

Ziggah

2020

Geodesy and Cartography

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Machine learning algorithms have emerged as a new paradigm shift in geoscience computations and applications. The present study aims to assess the suitability of Group Method of Data Handling (GMDH) in coordinate transformation. The data used for the coordinate transformation constitute the Ghana national triangulation network which is based on the two-horizontal geodetic datums (Accra 1929 and Leigon 1977) utilised for geospatial applications in Ghana. The GMDH result was compared with other standard methods such as Backpropagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), 2D conformal, and 2D affine. It was observed that the proposed GMDH approach is very efficient in transforming coordinates from the Leigon 1977 datum to the official mapping datum of Ghana, i.e. Accra 1929 datum. It was also found that GMDH could produce comparable and satisfactory results just like the widely used BPNN and RBFNN. However, the classical transformation methods (2D affine and 2D conformal) performed poorly when compared with the machine learning models (GMDH, BPNN and RBFNN). The computational strength of the machine learning models’ is attributed to its self-adaptive capability to detect patterns in data set without considering the existence of functional relationships between the input and output variables. To this end, the proposed GMDH model could be used as a supplementary computational tool to the existing transformation procedures used in the Ghana geodetic reference network.

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“…1). It lies between latitudes 4°30′ N and 11 o N, and between longitudes 3 o W and 1 0 E. It has a land area of 238, 540 km 2 (Fosu et al, 2006) with the highest elevation range not exceeding 880 m above mean sea level located at mountain Afadjato in the Volta Region (Berry, 1995). The land has general characteristics of low grassland and savanna with a divided plateau at the South-Central part of the country (Baabereyir, 2009).…”

Section: Study Area and Datamentioning

confidence: 99%

“…The Golden Triangle comprises of three permanent reference stations fixed at the apex of the three largest cities situated at the Western, Ashanti and Greater Accra regions. The reference stations form a triangle of sides 200 km and radii coverage of 100 km with area coverage of 79857 km 2 representing 33.5 % of the total land area (238,540 km 2 ) for Ghana (Fosu et al, 2006).…”

Section: Study Area and Datamentioning

confidence: 99%

Coordinate Transformation between Global and Local Datums Based on Artificial Neural Network with K-Fold Cross-Validation: A Case Study, Ghana

Ziggah

Tierra

et al. 2019

Earth sci. res. j.

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The popularity of Artificial Neural Network (ANN) methodology has been growing in a wide variety of areas in geodesy and geospatial sciences. Its ability to perform coordinate transformation between different datums has been well documented in literature. In the application of the ANN methods for the coordinate transformation, only the train-test (hold-out cross-validation) approach has usually been used to evaluate their performance. Here, the data set is divided into two disjoint subsets thus, training (model building) and testing (model validation) respectively. However, one major drawback in the hold-out cross-validation procedure is inappropriate data partitioning. Improper split of the data could lead to a high variance and bias in the results generated. Besides, in a sparse dataset situation, the hold-out cross-validation is not suitable. For these reasons, the K-fold cross-validation approach has been recommended. Consequently, this study, for the first time, explored the potential of using K-fold cross-validation method in the performance assessment of radial basis function neural network and Bursa-Wolf model under data-insufficient situation in Ghana geodetic reference network. The statistical analysis of the results revealed that incorrect data partition could lead to a false reportage on the predictive performance of the transformation model. The findings revealed that the RBFNN and Bursa-Wolf model produced a transformation accuracy of 0.229 m and 0.469 m, respectively. It was also realised that a maximum horizontal error of 0.881 m and 2.131 m was given by the RBFNN and Bursa-Wolf. The obtained results per the cadastral surveying and plan production requirement set by the Ghana Survey and Mapping Division are applicable. This study will contribute to the usage of K-fold cross-validation approach in developing countries having the same sparse dataset situation like Ghana as well as in the geodetic sciences where ANN users seldom apply the statistical resampling technique.

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Global navigation satellite system (GNSS): a utility for sustainable development in Ghana

Cited by 5 publications

References 1 publication

A 3D Procrustean Approach to Transform WGS84 Coordinates to Ghana War Office 1926 Reference Datum

A 3D Procrustean Approach to Transform WGS84 Coordinates to Ghana War Office 1926 Reference Datum

Feasibility of Using Group Method of Data Handling (Gmdh) Approach for Horizontal Coordinate Transformation

Coordinate Transformation between Global and Local Datums Based on Artificial Neural Network with K-Fold Cross-Validation: A Case Study, Ghana

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