Refereed Journals

The standard forward transformation for the direct conversion of curvilinear geodetic coordinates (φ, γ, Η) to its associated Cartesian coordinates (E, N, Z) has become a major challenge in most countries. This is due to the non-existence of the ellipsoidal height (h) in the modelling of their local geodetic reference network. Numerous studies in the past and recent years have suggested various mathematical techniques for predicting and estimating local ellipsoidal heights. Primary data used for the studies comprises of topographic data obtained from a survey in the Ghana urban water supply project in the Greater Kumasi Metropolitan Area (GKMA).This study considered an empirical evaluation of soft computing techniques such as Back Propagation Artificial Neural Network (BPANN), Generalized Regression Neural Network (GRNN), Radial Basis Function Artificial Neural Network (RBFANN) and conventional methods such as Polynomial Regression Model (PRM), Autoregressive Integrated Moving Average (ARIMA) and Least Square Regression (LSR). The motive is to apply and assess for the first time in our study area, the working efficiency of the aforementioned techniques. Each model technique was assessed based on statistical hypothesis (F, t) tests and performance criteria indices such as arithmetic mean error (AME), arithmetic mean square error (AMSE), minimum and maximum error value, and arithmetic standard deviation (ASD). The statistical analysis of the results revealed that, RBFANN, GRNN, BPANN, LSR, ARIMA and PRM, successfully estimated the ellipsoidal heights for the study area. However, the ANN models (RBFANN, BPANN, GRNN) outperforms the conventional models (LSR, PRM, ARIMA) in terms of accuracy and precision in estimating the local ellipsoidal heights. Also, statistical findings revealed that RBFANN produced more reliable results compared with the other methods. The main conclusion drawn from this study is that, the method of using soft computing is very much promising and can be adopted to solve some of the major problems related to height issues in Ghana. This study seeks to contribute to the existing knowledge on establishing a precise geodetic vertical datum in Ghana for national heightening purpose.

In this study, an integrated approach of a GIS - based Least Cost Path Analysis (Dijsktra Algorithm) and Multi-criteria decision methods (MCDM) techniques comprising of ENTROPY and TOPSIS was employed for the selection of the optimal route from Aflao to Elubo based on calculation of the cost grid surface in the ArcGIS environment. Topographic data containing digital elevation models, forest reserves, drainage features, land use and settlement data were used for the study. The results showed a model of an optimal route with a length of 471.34 kilometers as against the 540.60 kilometer distance by road. Hence, saving a travel distance of 69.26 kilometers. Also, the proposed distance of the Trans-ECOWAS line was approximately 498 kilometers, which is about 26.66 kilometers further distance compared to the optimal route proposed by this studies. Hence, an economical route has been proposed in terms of time, travel and construction cost. The route passes through four (4) coastal regions. Towns located along the route in the Volta Region includes; Aflao, Tokpo, Mepe, Gefia and Weija, Ablekuma, Ofankor and Mobole are among the towns in the Greater Accra Region found along the stretch. In the Central Region; Efutu, Amissakrom, Ewuoyaa, Mankessim, Apaa and Gomoa Lome are located along the proposed route. Towns along the stretch in the Western Region includes Pataho, Ashiaem, Agege, Amoakwasuazo and completes at Elubo. The optimal route will achieve the lowest cost of railway construction based on calculation of the cost raster layers.