Predicting SolarWind Density from Sun Images by Means of a GNN-LSTM Based Encoder-Decoder Deep Network

Accurate prediction of solar wind density fluctuations is essential for space weather forecasting due to their significant impact on satellite operations, power grids, and communication systems. In this work, we present a novel, physics-free forecasting framework that relies exclusively on binary masks of solar surface active regions and historical solar wind density measurements at L1. Our architecture integrates a Graph Neural Network (GNN) to encode the topological structure of binary active region maps (derived from the SDO dataset provided by NASA) with the time series prediction power of a Long Short-Term Memory (LSTM) network for modeling the electron and proton densities at L1 (extracted from the OMNI dataset provided by NASA) from 2012 to 2014. Based on the graph representation of binary masks only, our model simplifies and lowers the number of parameters by a significant degree compared to conventional convolutional approaches, also surpassing their predictive power. Our model demonstrated superior performance over two CNN-based baselines (ConvLSTM and CNN-LSTM). The strength of our solution lies in the use of graph representations to preserve the spatial topology information that pixel-based methods tend to overlook. The results indicate that light topology-preserving models capable of delivering reliable solar wind density predictions are feasible, making it possible to have efficient onboard space weather warning systems.