Deep Learning Technology for Predicting Solar Flares from (Geostationary Operational Environmental Satellite) Data

Abstract

Solar activity, particularly solar flares can have significant detrimental effects on both space-borne and grounds based systems and industries leading to subsequent impacts on our lives. As a consequence, there is much current interest in creating systems which can make accurate solar flare predictions. This paper aims to develop a novel framework to predict solar flares by making use of the Geostationary Operational Environmental Satellite (GOES) X-ray flux 1-minute time series data. This data is fed to three integrated neural networks to deliver these predictions. The first neural network (NN) is used to convert GOES X-ray flux 1-minute data to Markov Transition Field (MTF) images. The second neural network uses an unsupervised feature learning algorithm to learn the MTF image features. The third neural network uses both the learned features and the MTF images, which are then processed using a Deep Convolutional Neural Network to generate the flares predictions. To the best of our knowledge, this work is the first flare prediction system that is based entirely on the analysis of pre-flare GOES X-ray flux data. The results are evaluated using several performance measurement criteria that are presented in this paper.

Authors and Affiliations

Tarek A M Hamad Nagem, Rami Qahwaji, Stan Ipson, Zhiguang Wang, Alaa S. Al-Waisy

Keywords

Related Articles

Identification–Oriented Control Designs with Application to a Wind Turbine Benchmark

Wind turbines are complex dynamic systems forced by stochastic wind disturbances, gravitational, centrifugal, and gyroscopic loads. Since their aerodynamics are nonlinear, wind turbine modelling is thus challenging. Ther...

Efficient Relay Selection Scheme based on Fuzzy Logic for Cooperative Communication

The performance of cooperative network can be increased by using relay selection technique. Therefore, interest in relay selection is sloping upward. We proposed two new relay selection schemes based on fuzzy logic for d...

High Lightweight Encryption Standard (HLES) as an Improvement of 512-Bit AES for Secure Multimedia

In today’s scenario, people share information to another people frequently using network. Due to this, more amount of information are so much private but some are less private. Therefore, the attackers or the hackers tak...

Developing Backward Chaining Algorithm of Inference Engine in Ternary Grid Expert System

The inference engine is one of main components of expert system that influences the performance of expert system. The task of inference engine is to give answers and reasons to users by inference the knowledge of expert...

Sentiment Analysis Challenges of Informal Arabic Language

Recently, there are wide numbers of users that use the social network like Twitter, Facebook, MySpace to share various kinds of resources, express their opinions, thoughts, messages in real time. Thus, increase the amoun...

Download PDF file
  • EP ID EP262018
  • DOI 10.14569/IJACSA.2018.090168
  • Views 78
  • Downloads 0

How To Cite

Tarek A M Hamad Nagem, Rami Qahwaji, Stan Ipson, Zhiguang Wang, Alaa S. Al-Waisy (2018). Deep Learning Technology for Predicting Solar Flares from (Geostationary Operational Environmental Satellite) Data. International Journal of Advanced Computer Science & Applications, 9(1), 492-498. https://europub.co.uk/articles/-A-262018