Authors - Manisha Mane, Gargee Nitin Rangnekar, Gayatri Kishore Kshirsagar, Adarsh Suresh Nikam Abstract - Monitoring and prediction of space weather have gained tremendous significance with the increasing reliance of the telecommunication and aviation sectors on satellite communication and navigation systems. The two sectors are very vulnerable to space weather occurrences because they can always interfere with the functioning of satellites, high-frequency radio communication, and GPS accuracy. To mitigate these exposures, we recommend that telecommunication and aviation companies utilize a machine learning Space Weather Dashboard to facilitate real-time data visualization and predictive analytics assistance. The proposed architecture employs Azure Workspace for data storage and management, Unreal Engine 5 for the production of high-fidelity graphics, and machine learning models developed in Python. Our approach is based on the utilization of Long Short-Term Memory networks (LSTMs) for historical space data, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Various types of weather data points like solar X-ray flux, solar wind speed, coronal mass ejections, interplanetary magnetic field measurements, solar energetic particles, ionospheric data, and auroral data are utilized to enhance prediction precision. The dashboard allows for actionable insights to be built for industry professionals and real-time monitoring of critical space weather parameters. Additionally, in consideration of how it can improve their contribution to operation safety, the research here addresses the forecasting power of some of the machine learning architectures used for space weather. Our comparative research affirms that improved forecasting results in effective warning and risk assessment. This is a wonderful benchmark for the aviation and telecommunications industries, improving situation awareness and round-the-clock operating continuity.
