Modelling Local Geometric Geoid using Soft Computing and Classical Techniques: A Case Study of the University of Mines and Technology (UMaT) Local Geodetic Reference Network

International Journal of Earth Sciences Knowledge and Applications

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Title Modelling Local Geometric Geoid using Soft Computing and Classical Techniques: A Case Study of the University of Mines and Technology (UMaT) Local Geodetic Reference Network
 
Creator Kumi-Boateng, Bernard
Peprah, Michael Stanley
 
Description Geoid determination for national heighting is one of the major research focuses in geodetic sciences. Many studies in the past and recent years have suggested various mathematical techniques for local geometric geoid modelling. This study considered an empirical evaluation of soft computing techniques such as Backpropagation Artificial Neural Network (BPANN), Multivariate Adaptive Regression Spline (MARS), Generalized Regression Neural Network (GRNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and conventional methods such as Polynomial Regression Model (PRM), and Multiple Linear Regression (MLR). 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 performance criteria indices such as mean error (ME), mean square error (MSE), minimum and maximum error value (rmin and rmax), correlation coefficient (R), coefficient of determination (R2) and standard deviation (SD). The statistical analysis of the results revealed that ANFIS, GRNN, MARS, BPANN, MLR and PRM, successfully estimate the geoid heights with a good precision for the study area. However, ANFIS outperforms BPANN, MARS, MLR, PRM, and GRNN in estimating a local geoid height. In terms of ME and SD, ANFIS achieved 0.0445 m and 0.0013m as compared to BPANN, MARS, MLR, PRM, and GRNN which achieved 0.1462 m, 0.0059 m, 0.1423 m, 0.0148 m, 0.3117 m, 0.0102 m, 0.1798 m, 0.0208 m, 0.0878 m and 0.0023, respectively. The main conclusion drawn from this study is that, the method of using soft computing is promising and can be adopted to solve some of the major problems related to height issues in Ghana.
 
Publisher Ekrem KALKAN
 
Date 2020-10-15
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://ijeska.com/index.php/ijeska/article/view/42
 
Source International Journal of Earth Sciences Knowledge and Applications; Vol 2 No 3 (2020): Vol 2 Issue 3; 166-177
2687-5993
 
Language eng
 
Relation http://ijeska.com/index.php/ijeska/article/view/42/28
 
Rights Copyright (c) 2020 Michael Stanley Peprah, Bernard Kumi-Boateng
http://creativecommons.org/licenses/by-nc-nd/4.0
 

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