Application of a metaheuristic gradient‐based optimizer algorithm integrated into artificial neural network model in a local geoid modeling with global navigation satellite systems/leveling measurements

Abstract

In the present article, the efficiency of a new hybrid learning method, named artificial neural network with gradient‐based optimizer algorithm (ANN‐GBO), is investigated to determine a local geoid. The outcomes of the assessed method are compared with classical ANN (without GBO), some metaheuristic‐based ANN models and other study results (interpolation methods). Four commonly used performance metrics, root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE) and coefficient of determination (R2$$ {R}^2 $$) have been used to assess the applied methods. Assessment of predictions revealed that ANN‐GBO yielded the best results (RMSE: 10.51 cm, MAE: 8.26 cm, MARE: 0.27 cm, and R2$$ {R}^2 $$: 0.9622). The outcomes of the work clearly show that the ANN‐GBO approach has the lowest prediction error compared with ANN. It can be said that the GBO algorithm enhances the ANN capability in local geoid modeling. It may be recommended to optimize the weights of the ANN with GBO for high prediction accuracy. This research can be extended to other regions.

Investigating the Efficiency of Deep Learning Methods in Estimating GPS Geodetic Velocity

“…2019). Geoid undulation determination with ANN has root mean square error of 10.81 cm (Konakoglu et al., 2022). Ionospheric variations with ANN has an R value greater than 0.74 (Inyurt and Sekertekin.…”