Hybrid Unsupervised–Supervised Learning Framework for Rainfall Prediction Using Satellite Signal Strength Attenuation - Popphon Laon; Tanawit Sahavisit; Supavee Pourbunthidkul; Sarut Puangragsa; Pattharin Wichittrakarn; Pattarapong Phasukkit; Nongluck Houngkamhang (Sensors, 2026)
values exceeding 0.92 across all atmospheric regimes. Comparative analysis confirms LSTM superiority over RNN and GRU. Classification performance evaluation reveals exceptional detection capabilities with Probability of Detection ranging from 0.75 to 0.99 and False Alarm Ratios below 0.23. This work presents a scalable approach to weather radar systems for tropical regions with limited ground-based infrastructure, particularly during rapid meteorological transitions characteristic of tropical climates.
Keywords: Precipitation Measurement and Analysis; Meteorological Phenomena and Simulations; GNSS positioning and interference