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

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 Positionin

Landfill Lifespan Estimation: A Case Study

Municipal Solid Waste (MSW) management is one of the most serious environmental challenges facing the world at large due to the decomposing effect from the toxic gases being released into the environment by the MSW. The siting of landfill in any environment is a vital consideration that must be looked at due to the many factors such as the lifespan of the landfill, site selection, design, construction, operation and management. For this reason, it is important to estimate the lifespan of landfill accurately so as to explore the risk involved in acquiring new lands for landfills. Moreover, it is also necessary to consider proper methodology for estimating the lifespan of landfills. Based on these factors enumerated, various researchers have performed several laboratory tests in order to conclude on appropriate model that could be used to predict the lifespan of modern landfills. Mathematical models or expressions have also been suggested in literature as an alternative approach to the estimation of landfills lifespan. This research used the future value of money equation to estimate the lifespan of the Aboso landfill in Tarkwa, Ghana. The result showed that the landfill could operate for the next twelve years before it could exhaust its usefulness. Keywords: Landfill, Municipal Solid Waste, Lifespan Estimatio

Analysis of Methods for Ellipsoidal Height Estimation – The Case of a Local Geodetic Reference Network

Ghana’s local geodetic reference network is based on the War Office 1926 ellipsoid with data in latitude, longitude and orthometric height  without the existence of ellipsoidal height. This situation makes it difficult to apply the standard forward transformation equation for direct conversion of curvilinear geodetic coordinates to its associated cartesian coordinates (X, Y, Z) in the Ghana local geodetic reference network. In order to overcome such a challenge, researchers resort to various techniques to obtain the ellipsoidal height for a local geodetic network. Therefore, this paper evaluates, compares, and discusses different methods for estimating ellipsoidal height for a local geodetic network. The investigated methods are the Abridged Molodensky transformation model, Earth Gravitational Model, and the Orthometric Height approach. To evaluate these methods, their estimated local ellipsoidal height values were implemented in the seven-parameter similarity transformation model of Bursa-Wolf. The performance of each of the methods was assessed based on statistical indicators of Mean Square Error (MSE), Mean Absolute Error (MAE), Horizontal Position Error (HE) and Standard Deviation (SD). The statistical findings revealed that, the Abridged Molodensky model produced more reliable transformation results compared with the other methods. It can be concluded that for Ghana’s local geodetic network, the most practicable method for estimating ellipsoidal height is the Abridged Molodensky transformation model.  Keywords: Abridged Molodensky Model, Earth Gravitational Model, Orthometric Height, Geodetic Networ

Performance Evaluation of Training Algorithms in Backpropagation Neural Network Approach to Blast-Induced Ground Vibration Prediction

Abstract Backpropagation Neural Network (BPNN) is an artificial intelligence technique that has seen several applications in many fields of science and engineering. It is well-known that, the critical task in developing an effective and accurate BPNN model depends on an appropriate training algorithm, transfer function, number of hidden layers and number of hidden neurons. Despite the numerous contributing factors for the development of a BPNN model, training algorithm is key in achieving optimum BPNN model performance. This study is focused on evaluating and comparing the performance of 13 training algorithms in BPNN for the prediction of blast-induced ground vibration. The training algorithms considered include: Levenberg-Marquardt, Bayesian Regularisation, Broyden–Fletcher–Goldfarb–Shanno (BFGS) Quasi-Newton, Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Fletcher-Powell Conjugate Gradient, Polak-Ribiére Conjugate Gradient, One Step Secant, Gradient Descent with Adaptive Learning Rate, Gradient Descent with Momentum, Gradient Descent, and Gradient Descent with Momentum and Adaptive Learning Rate. Using ranking values for the performance indicators of Mean Squared Error (MSE), correlation coefficient (R), number of training epoch (iteration) and the duration for convergence, the performance of the various training algorithms used to build the BPNN models were evaluated. The obtained overall ranking results showed that the BFGS Quasi-Newton algorithm outperformed the other training algorithms even though the Levenberg Marquardt algorithm was found to have the best computational speed and utilised the smallest number of epochs.   Keywords: Artificial Intelligence, Blast-induced Ground Vibration, Backpropagation Training Algorithm

Appraisal of ANN and ANFIS for Predicting Vertical Total Electron Content (VTEC) in the Ionosphere for GPS Observations

Positional accuracy in the usage of GPS receiver is one of the major challenges in GPS observations. The propagation of the GPS signals are interfered by free electrons which are the massive particles in the ionosphere region and results in delays in the transmission of signals to the Earth. Therefore, the total electron content is a key parameter in mitigating ionospheric effects on GPS receivers. Many researchers have therefore proposed various models and methods for predicting the total electron content along the signal path. This paper focuses on the use of two different models for predicting the Vertical Total Electron Content (VTEC). Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) algorithms have been developed for the prediction of VTEC in the ionosphere.  The developed ANN and ANFIS model gave Root Mean Square Error (RMSE) of 1.953 and 1.190 respectively.  From the results it can be stated that the ANFIS is more suitable tool for the prediction of VTEC. Keywords: Artificial Neural Network, Adaptive Neuro Fuzzy Inference System, Vertical Total Electro

A THRESHOLD-FREE FILTERING ALGORITHM FOR AIRBORNE LIDAR POINT CLOUDS BASED ON EXPECTATION-MAXIMIZATION

Filtering is a key step for most applications of airborne LiDAR point clouds. Although lots of filtering algorithms have been put forward in recent years, most of them suffer from parameters setting or thresholds adjusting, which will be time-consuming and reduce the degree of automation of the algorithm. To overcome this problem, this paper proposed a threshold-free filtering algorithm based on expectation-maximization. The proposed algorithm is developed based on an assumption that point clouds are seen as a mixture of Gaussian models. The separation of ground points and non-ground points from point clouds can be replaced as a separation of a mixed Gaussian model. Expectation-maximization (EM) is applied for realizing the separation. EM is used to calculate maximum likelihood estimates of the mixture parameters. Using the estimated parameters, the likelihoods of each point belonging to ground or object can be computed. After several iterations, point clouds can be labelled as the component with a larger likelihood. Furthermore, intensity information was also utilized to optimize the filtering results acquired using the EM method. The proposed algorithm was tested using two different datasets used in practice. Experimental results showed that the proposed method can filter non-ground points effectively. To quantitatively evaluate the proposed method, this paper adopted the dataset provided by the ISPRS for the test. The proposed algorithm can obtain a 4.48 % total error which is much lower than most of the eight classical filtering algorithms reported by the ISPRS

2D Cadastral Coordinate Transformation using extreme learning machine technique

Land surveyors, photogrammetrists, remote sensing engineers and professionals in the Earth sciences are often faced with the task of transferring coordinates from one geodetic datum into another to serve their desired purpose. The essence is to create compatibility between data related to different geodetic reference frames for geospatial applications. Strictly speaking, conventional techniques of conformal, affine and projective transformation models are mostly used to accomplish such task. With developing countries like Ghana where there is no immediate plans to establish geocentric datum and still rely on the astro-geodetic datums as it national mapping reference surface, there is the urgent need to explore the suitability of other transformation methods. In this study, an effort has been made to explore the proficiency of the Extreme Learning Machine (ELM) as a novel alternative coordinate transformation method. The proposed ELM approach was applied to data found in the Ghana geodetic reference network. The ELM transformation result has been analysed and compared with benchmark methods of backpropagation neural network (BPNN), radial basis function neural network (RBFNN), two-dimensional (2D) affine and 2D conformal. The overall study results indicate that the ELM can produce comparable transformation results to the widely used BPNN and RBFNN, but better than the 2D affine and 2D conformal. The results produced by ELM has demonstrated it as a promising tool for coordinate transformation in Ghana