Machine Learning Prediction of Rain-Induced Signal Loss for Resilient Satellite Communication in the Tropics

Satellite communication is vital for global services such as internet access, weather forecasting, and military operations. However, systems operating above 10 GHz are highly affected by rain-induced signal loss, especially in tropical regions. This study introduces a machine learning-based approach to predict rain attenuation using linear regression, polynomial regression, and artificial neural networks (ANN). Rain attenuation data was generated using the Synthetic Storm Technique (SST) with rainfall measurements from 2019 to 2022 at University Technical Malaysia Melaka (UTeM). Model performance was evaluated against the ITU-R P.618-13 and the Simple Attenuation Model (SAM). The ANN model showed the highest accuracy, achieving an RMSE of 0.98 dB and R² of 0.93 at 0.1% and 0.01% exceedance probabilities. The results demonstrate the potential of machine learning to improve communication reliability and support climate-resilient infrastructure planning in high-rainfall regions.