Abstract

ZIGGAH, Yao Yevenyo; YOUJIAN, Hu; TIERRA, Alfonso Rodrigo  and  LAARI, Prosper Basommi. Coordinate Transformation between Global and Local Data Based on Artificial Neural Network with K-Fold Cross-Validation in Ghana. Earth Sci. Res. J. [online]. 2019, vol.23, n.1, pp.67-77. ISSN 1794-6190.  https://doi.org/10.15446/esrj.v23n1.63860.

The popularity of Artificial Neural Network (ANN) methodology has been growing in a wide variety of areas in geodesy and geospatial sciences. Its ability to perform coordinate transformation between different data has been well documented in literature. In the application of the ANN methods for the coordinate transformation, only the train-test (hold-out cross-validation) approach has usually been used to evaluate their performance. Here, the data set is divided into two disjoint subsets thus, training (model building) and testing (model validation) respectively. However, one major drawback in the hold-out cross-validation procedure is inappropriate data partitioning. Improper split of the data could lead to a high variance and bias in the results generated. Besides, in a sparse dataset situation, the hold-out cross-validation is not suitable. For these reasons, the K-fold cross-validation approach has been recommended. Consequently, this study, for the first time, explored the potential of using K-fold cross-validation method in the performance assessment of radial basis function neural network and Bursa-Wolf model under data-insufficient situation in Ghana geodetic reference network. The statistical analysis of the results revealed that incorrect data partition could lead to a false reportage on the predictive performance of the transformation model. The findings revealed that the RBFNN and Bursa-Wolf model produced a root mean square horizontal positional error of 0.797 m and 1.182 m, respectively. The RBFNN model results per the cadastral surveying and plan production requirement set by the Ghana Survey and Mapping Division are applicable. This study will contribute to the usage of K-fold cross-validation approach in developing countries having the same sparse dataset situation like Ghana and in the geodetic sciences where ANN users seldom apply the statistical resampling technique.

Keywords : Radial basis function neural network; Bursa-Wolf model; K-fold cross-validation; Coordinate transformation; Statistical Resampling.

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