GA-optimized neural network for forecasting the geomagnetic storm index

Abstract

A method that combines an artificial neural network and a genetic algorithm (ANN+GA) was developed in order to forecast the disturbance storm time (Dst) index. This technique involves optimizing the ANN by GA to update the ANN weights and to forecast the short-term Dst index from 1 to 6 hours in advance by using the time series values of the Dst and auroral electrojet (AE) indices. The database used contains 233,760 hourly geomagnetic indices data from 00 UT on 01 January 1990 to 23 UT on 31 August 2016. Different topologies of ANN were analyzed and the optimum architecture was selected. It emerged that the proposed ANN+GA method can be properly trained for forecasting Dst (t+1 to t+6) with good accuracy (with root mean square errors RMSE ≤ 10nT and correlation coefficients R ≥ 0.9), and that the utilized geomagnetic indices significantly affect the good training and predicting capabilities of the chosen network. The results show a good agreement between the measured and modeled Dst variations in both the main and recovery phases of a geomagnetic storm.