Short-term prediction of Forbush decrease indices with Adaptive Neuro-Fuzzy Inference Systems

Forbush decrease is a rapid decrease in the observed galactic cosmic ray intensity pattern followed by a coronal mass ejection. In the present paper we have analyzed the daily sampled Forbush decrease indices from January, 1967 to December, 2003 generated in IZMIRAN, Russia. Prediction of this astrophysical parameter has been performed using Takagi-Sugeno type Adaptive Neuro-Fuzzy Inference Systems (ANFIS) that allows reduction both in statistical and systematic uncertainties. The visual observation based on the graphical comparison between observed and predicted values and the qualitative performance assessment of the model indicates that this method can be used effectively for minimum Forbush decrease indices predictions. In this work, bell-shaped Gauss type membership functions have been found suitable and Hybrid Learning Algorithm method has been used for the optimization. To judge the predictive capability of the developed methodology, based on ANFIS model, the performance indicators show that root mean square error value is 0.002505 for training and 0.002502 for testing period. So, it may establish that the Forbush effect as a storm in cosmic rays is interestingly very much predictable in short senses.