Geoid Determination Based on Log Sigmoid Function of Artificial Neural Networks: (A case Study: Iran)

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Abstract

A Back Propagation Artificial Neural Network (BPANN) is a well-known learning algorithm
predicated on a gradient descent method that minimizes the square error involving the network
output and the goal of output values. In this study, 261 GPS/Leveling and 8869 gravity intensity
values of Iran were selected, then the geoid with three methods “ellipsoidal stokes integral”,
“BPANN”, and “collocation” were evaluated. Finally obtained results were compared and best
the method was introduced. In Iran, the consequences showed that “BPANN” has been superior
than other methods. Root Mean Square Error of this algorithm was less than ±0.292 m.
Therefore, we concluded that BPANN can be used for geoid determination as an excellent
alternative to the classic methods.

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