Novel Ellipsoidal Heights Predictive Models Based on Artificial Intelligence Training Algorithms and Classical Regression Models Techniques: A Case Study in the Greater Kumasi Metropolitan Area Local Geodetic Reference Network, Kumasi, Ghana: A Case Study in the Greater Kumasi Metropolitan Area (GKMA) Local Geodetic Reference Network, Kumasi, Ghana

Year 2022, Volume: 4 Issue: 3, 493 - 515, 30.12.2022

Naa Lamkaı Daniel Asenso-gyambıbı Michael Stanley Peprah Edwin Kojo Larbı Benedict Asamoah Philip Okantey

Abstract

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.

Keywords

Artificial Intelligence, Geodetic Reference Network, Height systems, Performance Criteria Indices, Statistical Hypothesis, Regression Models

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