Low Dimensional Convolutional Neural Network For Solar Flares GOES Time Series Classification

TL;DR

This study develops a low-dimensional 1D CNN model to forecast solar flare probabilities using GOES X-ray time series data, demonstrating competitive accuracy and analyzing the impact of data selection methods.

Contribution

Introduces a novel low-dimensional CNN architecture for solar flare prediction and compares its performance with state-of-the-art models across different data selection scenarios.

Findings

Chronological data selection slightly reduces model accuracy.

Model performs well with X-ray data alone compared to other methods.

Model cannot distinguish between M and X class flares when using only X-ray data.

Abstract

Space weather phenomena such as solar flares, have massive destructive power when reaches certain amount of magnitude. Such high magnitude solar flare event can interfere space-earth radio communications and neutralize space-earth electronics equipment. In the current study, we explorer the deep learning approach to build a solar flare forecasting model and examine its limitations along with the ability of features extraction, based on the available time-series data. For that purpose, we present a multi-layer 1D Convolutional Neural Network (CNN) to forecast solar flare events probability occurrence of M and X classes at 1,3,6,12,24,48,72,96 hours time frame. In order to train and evaluate the performance of the model, we utilised the available Geostationary Operational Environmental Satellite (GOES) X-ray time series data, ranged between July 1998 and January 2019, covering almost entirely the solar cycles 23 and 24. The forecasting model were trained and evaluated in two different scenarios (1) random selection and (2) chronological selection, which were compare afterward. Moreover we compare our results to those considered as state-of-the-art flare forecasting models, both with similar approaches and different ones.The majority of the results indicates that (1) chronological selection obtain a degradation factor of 3\% versus the random selection for the M class model and elevation factor of 2\% for the X class model. (2) When consider utilizing only X-ray time-series data, the suggested model achieve high score results compare to other studies. (3) The suggested model combined with solely X-ray time-series fails to distinguish between M class magnitude and X class magnitude solar flare events. All source code are available at https://github.com/vladlanda/Low-Dimensional-Convolutional-Neural-Network-For-Solar-Flares-GOES-Time-Series-Classification