Abstract
Abstract The present study computes B-W extension model (extended Bursa-Wolf model) coordinate transformation parameters from World Geodetic System 1984 (WGS-84) to the Everest datum namely Everest (1830) and Everest (1956) using records of coordinate measurements from Global Positioning System (GPS) observable across Nepal region. Synthetic or modeled coordinates were determined by using the Artificial Neural Network (ANN) and Singular Value Decomposition (SVD) methods. We studied 9-transformation parameters with the help of the ANN technique and validated the outcomes with the SVD method. The comparative analysis of the ANN, as well as SVD methods, was done with the observed output following one way ANOVA test. The analysis showed that the null hypothesis for both datums were acceptable and suggesting all models statistically significantly equivalent to each other. The outcomes from this study would complement a relatively better understanding of the techniques for coordinate transformation and precise coordinate assignment while assimilating data sets from different resources.
Highlights
The geocentric datum Everest (1830) and Everest (1956) are the o cial Indian continent coordinate systems and have been implemented for more than 150 years
Synthetic or modeled coordinates were determined by using the Arti cial Neural Network (ANN) and Singular Value Decomposition (SVD) methods
We studied 9-transformation parameters with the help of the ANN technique and validated the outcomes with the SVD method
Summary
The geocentric datum Everest (1830) and Everest (1956) are the o cial Indian continent coordinate systems and have been implemented for more than 150 years. These datums played an important role in the geodetic studies of the Indian continent. Yj are the hidden layer nodes and z , z . Zk are the output nodes a simple BPANN architecture can be given like Fig. 2. In the gure the red circles showed input layer nodes, blue circles indicate hidden layer nodes and green circles are the symbols of output layer nodes
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