Abstract
Machine learning algorithms have emerged as a new paradigm shift in geoscience computations and applications. The present study aims to assess the suitability of Group Method of Data Handling (GMDH) in coordinate transformation. The data used for the coordinate transformation constitute the Ghana national triangulation network which is based on the two-horizontal geodetic datums (Accra 1929 and Leigon 1977) utilised for geospatial applications in Ghana. The GMDH result was compared with other standard methods such as Backpropagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), 2D conformal, and 2D affine. It was observed that the proposed GMDH approach is very efficient in transforming coordinates from the Leigon 1977 datum to the official mapping datum of Ghana, i.e. Accra 1929 datum. It was also found that GMDH could produce comparable and satisfactory results just like the widely used BPNN and RBFNN. However, the classical transformation methods (2D affine and 2D conformal) performed poorly when compared with the machine learning models (GMDH, BPNN and RBFNN). The computational strength of the machine learning models’ is attributed to its self-adaptive capability to detect patterns in data set without considering the existence of functional relationships between the input and output variables. To this end, the proposed GMDH model could be used as a supplementary computational tool to the existing transformation procedures used in the Ghana geodetic reference network.
Highlights
Machine learning regression and classification techniques have recently been embraced and applied in various fields of geosciences
Based on the Group Method of Data Handling (GMDH) reported mathematical conveniences, this paper explores the capability of GMDH as a coordinate transformation procedure in Ghana’s geodetic reference network
The SDRHD (Table 2) values signifies a high measure of precision of the GMDH predictions and explain the extent they differ from the average residual horizontal distance
Summary
Machine learning regression and classification techniques have recently been embraced and applied in various fields of geosciences. These methods have been touted by many scholars as a new paradigm shift in geoscientific computations (Angiuli et al, 2006; El-Assal et al, 2011; Ali et al, 2004). The researcher’s objective is to investigate whether the machine learning methods can serve as a dependable alternative to the traditional procedures for solving geodetic problems. The last, coordinate transformation, is the main interest in the present study
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call