A GP-MOEA/D Approach for Modelling Total Electron Content over Cyprus

TL;DR

This paper introduces a multi-objective genetic programming approach to accurately model and predict Total Electron Content variations over Cyprus, outperforming other methods and aiding ionospheric error correction in positioning.

Contribution

The paper presents a novel GP-MOEA/D method for modeling vTEC, balancing accuracy and generalization, and demonstrates its superiority over existing models using 11 years of data.

Findings

GP-MOEA/D outperforms single-objective GP and neural network models.

The model effectively captures diurnal, seasonal, and long-term vTEC variations.

Experimental results show high approximation accuracy over 11 years.

Abstract

Vertical Total Electron Content (vTEC) is an ionospheric characteristic used to derive the signal delay imposed by the ionosphere on near-vertical trans-ionospheric links. The major aim of this paper is to design a prediction model based on the main factors that influence the variability of this parameter on a diurnal, seasonal and long-term time-scale. The model should be accurate and general (comprehensive) enough for efficiently approximating the high variations of vTEC. However, good approximation and generalization are conflicting objectives. For this reason a Genetic Programming (GP) with Multi-objective Evolutionary Algorithm based on Decomposition characteristics (GP-MOEA/D) is designed and proposed for modeling vTEC over Cyprus. Experimental results show that the Multi-Objective GP-model, considering real vTEC measurements obtained over a period of 11 years, has produced a good approximation of the modeled parameter and can be implemented as a local model to account for the ionospheric imposed error in positioning. Particulary, the GP-MOEA/D approach performs better than a Single Objective Optimization GP, a GP with Non-dominated Sorting Genetic Algorithm-II (NSGA-II) characteristics and the previously proposed Neural Network-based approach in most cases.