Evaluation of global earth gravity models and corrector surface techniques for GNSS/leveling-derived geoid fitting in Ethiopia

Abstract The establishment of a reliable vertical reference framework remains a fundamental challenge in geodetically complex regions such as Ethiopia, where extreme topographic gradients and lithospheric heterogeneity severely compromise the predictive accuracy of Global gravity models (GGMs). This study presents a hybrid geoid modeling framework that integrates a large nationwide dataset comprising 92,433 airborne gravity observations and 22,370 terrestrial gravity points to refine GGM performance across the Ethiopian Plateau. Initial spectral analysis identified Earth Gravitational Model 2008 (GM2008) as the most suitable global baseline; however, it exhibited a significant Root Mean Square Error (RMSE) of 0.9239 m, primarily attributed to omission errors in resolving high-frequency gravitational signals. To mitigate these discrepancies, we evaluated six corrector surface architectures, ranging from classical parametric transformations to advance nonlinear machine learning algorithms. Our results demonstrate that a feedforward Artificial Neural Network (ANN) based on a multilayer perceptron (MLP) architecture provides an improved stochastic representation of the geopotential surface, achieving a 94.24 % reduction in the overall prediction error. By adaptively modeling nonlinear residuals, the MLP model refined the local hybrid geoid achieving an RMSE of 0.0532 m which represents a significant improvement compared to traditional parametric models. This research provides a scalable, methodological framework for the modernization of National Vertical Datums in similar topographically and geodynamically complex regions.