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

Simple item page
dc.contributor.authorPopphon Laon
dc.contributor.authorTanawit Sahavisit
dc.contributor.authorSupavee Pourbunthidkul
dc.contributor.authorSarut Puangragsa
dc.contributor.authorPattharin Wichittrakarn
dc.contributor.authorPattarapong Phasukkit
dc.contributor.authorNongluck Houngkamhang
dc.date.accessioned2026-05-08T19:26:07Z
dc.date.issued2026-1-18
dc.description.abstractvalues 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.doi10.3390/s26020648
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/20440
dc.publisherSensors
dc.subjectPrecipitation Measurement and Analysis
dc.subjectMeteorological Phenomena and Simulations
dc.subjectGNSS positioning and interference
dc.titleHybrid Unsupervised–Supervised Learning Framework for Rainfall Prediction Using Satellite Signal Strength Attenuation
dc.typeArticle

Files

Collections

All