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Lilje, M., “Geodesy and Surveying in the future – the importance of heights”, LMV Rep. 1999.3, National Land Survey, Gavle, Sweden, 1999, 418.

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Article

Appraisal of Methods for Estimating Orthometric Heights – A Case Study in a Mine

1Department of Geomatic Engineering, University of Mines and Technology, Tarkwa-Western Region, Ghana

2Department of Geological Engineering, University of Mines and Technology, Tarkwa-Western Region, Ghana


Journal of Geosciences and Geomatics. 2017, Vol. 5 No. 3, 96-108
DOI: 10.12691/jgg-5-3-1
Copyright © 2017 Science and Education Publishing

Cite this paper:
M. S. Peprah, S. A. Kumi. Appraisal of Methods for Estimating Orthometric Heights – A Case Study in a Mine. Journal of Geosciences and Geomatics. 2017; 5(3):96-108. doi: 10.12691/jgg-5-3-1.

Correspondence to: M.  S. Peprah, Department of Geomatic Engineering, University of Mines and Technology, Tarkwa-Western Region, Ghana. Email: michaelstanleypeprah@ymail.com

Abstract

The concept of orthometric heights system determination plays a major key role in geodesy, and it has broad applications in various fields and activities. In geodesy, one significant quantity is the orthometric height, the height above or below the geoid along the gravity plumbline. Conventionally, the orthometric height is determined by gravimetry and levelling techniques. However, the aforementioned techniques has its own demerits. Thus, the error is accumulated with the increase of the propagation measurement line, it is difficult to convert two separated points which is located in two continents or islands separated by sea. These techniques are tedious, time consuming and expensive. In order to resolve this challenge, many researchers resort to various techniques and approaches of obtaining orthometric heights for an area using various mathematical models. It is in this quest that, this study seek to estimate orthometric heights of a mine by utilizing plausible alternative techniques based on artificial neural networks (ANN), multivariate adaptive regression splines (MARS), polynomial regression models and multiple linear regression (MLR). The working efficiency and performance of each model has been assessed based on statistical indicators of Mean (M), Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Bias Error (MBE), Mean Absolute Error (MAE), Standard Deviation (SD), Correlation coefficient (R), Correction of determination (R2), and Signal to Noise Ratio (SNR). The statistical findings reveal that all the models produce satisfactory results in estimating the orthometric heights in the mine. MARS and ANN models compare to the MLR and polynomial models achieved higher results in terms of accuracy with mean and standard deviation of -0.000001888 m, +2.24736 m, and +0.005835 m and 0.095063 m respectively. This study will create the opportunity for geospatial practitioners to recognize the significant of ANN, MARS, MLR, and Polynomial model in solving some of the problems in geoscientific community.

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