Fault-Tolerant Space Weather Prediction: Leveraging Raw DSCOVR Data with Long Short-Term Memory in Machine Learning

In the realm of space weather forecasting, the emergence of our proposed Helios-LSTM algorithm signifies a ground-breaking leap towards precision in predicting solar wind activity. With a paramount focus on the urgent requirement for accurate forecasts, this paper introduces a cutting-edge deep learning model that not only monitors solar wind patterns but achieves an unprecedented 94% accuracy rate. Our proposed research stems from a meticulous integration of data from NASA's Solar Wind, Solar Radiation (CME), and Geomagnetic Storm APIs, culminating in a robust dataset designed for training our proposed novice model. Our methodology encompasses sophisticated data preprocessing techniques, leveraging hourly features from solar wind data and employing imputation strategies for missing values. The core of the model architecture includes a Bidirectional LSTM layer to capture nuanced temporal dependencies, three dense layers for comprehensive feature transformation, and a GRU layer to further enhance the analysis of solar wind activity. Trained on 29 features, our Helios-LSTM algorithm not only outperforms existing methods but also demonstrates its prowess in predicting solar wind patterns over varying time intervals - from the last two hours to the last seven days. The significance of our research extends beyond the realm of solar wind forecasting, as solar wind interactions with Earth's magnetic field can trigger geomagnetic storms, presenting imminent risks to critical infrastructure. By forecasting the Disturbance Storm-Time Index (Dst), our novice model utilizes data from NASA's ACE and NOAA's DSCOVR satellites to unravel the complex relationships between interplanetary magnetic fields, solar wind plasma, and sunspot activity. Evaluation metrics such as root mean square error & coefficient of determination substantiate our proposed novel Helios-LSTM model's efficacy in predicting geomagnetic storms. The outcomes not only offer invaluable insights for satellite operators, power grid managers, and navigation systems but also lay the foundation for a predictive model that safeguards Earth against the disruptive impacts of geomagnetic storms. Our research heralds a new era in space weather forecasting, providing decision-makers with a robust and timely tool to fortify essential systems and brace for geomagnetic disturbances.