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

Olivia Ong Yi Hui, Mawarni Mohamed Yunus, Mas Haslinda Mohamad and Jafri Din
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Olivia Ong Yi Hui: Centre of Technology for Disaster Risk Reduction, Faculty Technology dan Kejuruteraan Elektronik dan Computer, University Technical Malaysia Melaka, Jalan Hang Tuah Jaya, Durian Tunggal, Melaka, Malaysia
Mawarni Mohamed Yunus: Centre of Technology for Disaster Risk Reduction, Faculty Technology dan Kejuruteraan Elektronik dan Computer, University Technical Malaysia Melaka, Jalan Hang Tuah Jaya, Durian Tunggal, Melaka, Malaysia
Mas Haslinda Mohamad: Centre for Telecommunication Research and Innovation, Faculty Technology dan Kejuruteraan Elektronik dan Computer, University Technical Malaysia Melaka, Jalan Hang Tuah Jaya, Durian Tunggal, Melaka, Malaysia
Jafri Din: Wireless Communication Center, Faculty Kejuruteraan Electric, University Technology Malaysia, Sukadi, Johor Bahru, Malaysia

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 6, 3435-3442

Abstract: 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.

Date: 2025
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