Published

2019-01-01

Coordinate Transformation between Global and Local Datums Based on Artificial Neural Network with K-Fold Cross-Validation: A Case Study, Ghana

Transformación coordinada entre información global y local basada en Redes Neuronales Artificiales con validación cruzada de k-iteraciones en Ghana

DOI:

https://doi.org/10.15446/esrj.v23n1.63860

Keywords:

Radial basis function neural network, Bursa-Wolf model, K-fold cross-validation, Coordinate transformation, Statistical Resampling (en)
función de base radial en redes neuronales, modelo Bursa-Wolf, validación cruzada de k-iteracciones, transformación coordinada, remuestreo estadístico, (es)

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Authors

  • Yao Yevenyo Ziggah China University of Geosciences (Wuhan); University of Mines and Technology https://orcid.org/0000-0002-9940-1845
  • Hu Youjian China University of Geosciences (Wuhan)
  • Alfonso Rodrigo Tierra Universidad de las Fuerzas Armadas ESPE
  • Prosper Basommi Laari University for Development Studies
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 datums 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 transformation accuracy of 0.229 m and 0.469 m, respectively. It was also realised that a maximum horizontal error of 0.881 m and 2.131 m was given by the RBFNN and Bursa-Wolf. The obtained 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 as well as in the geodetic sciences where ANN users seldom apply the statistical resampling technique.

La popularidad de la metodología de Redes Neuronales Artificiales está en crecimiento en varias áreas en geodesia y en las ciencias geoespaciales. Su capacidad de realizar una transformación coordinada entre diferente información ha sido bien documentada en la literatura. En la aplicación de métodos de Redes Neuronales Artificiales para la transformación coordinada solo se ha evaluado el desempeño del enfoque de prueba de adiestramiento (validación cruzada por método de retención). En este punto, la información se divide en dos subconjuntos diferentes: adiestramiento (modelo de construcción) y verificación (modelo de validación). Sin embargo, una desventaja en el procedimiento de validación cruzada por método de retención es inapropiada durante la división de información. Una partición no adecuada en la información podría llevar a una gran diferencia o a un sesgo en los resultados generados. Además, ante una situación de un conjunto de datos disperso la validación cruzada por método de retención no es adecuada. Por estas razones se recomienda la validación cruzada de k-iteraciones. Por consiguiente, este estudio, por primera vez, explora el potencial de usar el método por validación cruzada de k-iteraciones en la evaluación de ejecución de la función de base radial en redes neuronales y el modelo Bursa-Wolf en una situación de información insuficiente en la red de referencia geodética de Ghana. El análisis estadístico de los resultados muestra que una partición incorrecta de información puede llevar a un registro falso en la ejecución predictiva del modelo de transformación. Los resultados demuestran que la función radial y el modelo Bursa-Wolf producen un error posicional de media cuadrática horizontal de 0.797 m y 1.182 m, respectivamente. Los resultados del modelo radial por la medición cadastral concuerdan con los requerimientos del plan de producción instaurados por la divisón de mapeo del servicio geológico de Ghana. Este estudio contribuirá en la usabilidad del método de validación cruzada de k-iteracciones en países en desarrollo que tienen conjuntos de datos dispersos, como Ghana, y en las ciencias geodésicas donde los usuarios de redes neuronales casi nunca aplican la técnica estadística de remuestreo.

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Ziggah, Y. Y., Youjian, H., Tierra, A. R., & Laari, P. B. (2019). Coordinate Transformation between Global and Local Datums Based on Artificial Neural Network with K-Fold Cross-Validation: A Case Study, Ghana. Earth Sciences Research Journal, 23(1), 67-77. https://doi.org/10.15446/esrj.v23n1.63860

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