Hybrid Unsupervised–Supervised Learning Framework for Rainfall Prediction Using Satellite Signal Strength Attenuation

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dc.contributor.authorLaon, Popphon
dc.contributor.authorSahavisit, Tanawit
dc.contributor.authorPourbunthidkul, Supavee
dc.contributor.authorPuangragsa, Sarut
dc.contributor.authorWichittrakarn, Pattharin
dc.contributor.authorPhasukkit, Pattarapong
dc.contributor.authorHoungkamhang, Nongluck
dc.date.accessioned2026-08-06T10:54:10Z
dc.date.available2026-08-06T10:54:10Z
dc.date.issued2026-01-01
dc.description.abstractSatellite communication systems experience significant signal degradation during rain events, a phenomenon that can be leveraged for meteorological applications. This study introduces a novel hybrid machine learning framework combining unsupervised clustering with cluster-specific supervised deep learning models to transform satellite signal attenuation into a predictive tool for rainfall prediction. Unlike conventional single-model approaches treating all atmospheric conditions uniformly, our methodology employs K-Means Clustering with the Elbow Method to identify four distinct atmospheric regimes based on Signal-to-Noise Ratio (SNR) patterns from a 12-m Ku-band satellite ground station at King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand, combined with absolute pressure and hourly rainfall measurements. The dataset comprises 98,483 observations collected with 30-s temporal resolutions, providing comprehensive coverage of diverse tropical atmospheric conditions. The experimental platform integrates three subsystems: a receiver chain featuring a Low-Noise Block (LNB) converter and Software-Defined Radio (SDR) platform for real-time data acquisition; a control system with two-axis motorized pointing incorporating dual-encoder feedback; and a preprocessing workflow implementing data cleaning, K-Means Clustering (k = 4), Synthetic Minority Over-Sampling Technique (SMOTE) for balanced representation, and standardization. Specialized Long Short-Term Memory (LSTM) networks trained for each identified cluster enable capture of regime-specific temporal dynamics. Experimental validation demonstrates substantial performance improvements, with cluster-specific LSTM models achieving R<sup>2</sup> 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.
dc.identifier.citationSensors, 26(2), 2026
dc.identifier.doi10.3390/s26020648
dc.identifier.issn14248220
dc.identifier.other2-s2.0-105028827153
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17752
dc.sourceSensors
dc.subjectK-means clustering
dc.subjectlong short-term memory (LSTM)
dc.subjectrainfall prediction
dc.subjectsatellite communication
dc.subjectsignal-to-noise ratio (SNR)
dc.titleHybrid Unsupervised–Supervised Learning Framework for Rainfall Prediction Using Satellite Signal Strength Attenuation
dc.typeArticle

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