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

Popphon Laon1, Tanawit Sahavisit1, Supavee Pourbunthidkul1

  • 1School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.

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Summary

This study uses a hybrid machine learning model to predict rainfall by analyzing satellite signal degradation. The novel approach identifies distinct atmospheric conditions for improved accuracy in tropical regions.

Keywords:
K-means clusteringlong short-term memory (LSTM)rainfall predictionsatellite communicationsignal-to-noise ratio (SNR)

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Area of Science:

  • Meteorology
  • Satellite Communications
  • Machine Learning

Background:

  • Satellite signal degradation during rain offers meteorological insights.
  • Conventional models treat diverse atmospheric conditions uniformly, limiting accuracy.
  • Tropical regions often lack extensive ground-based weather infrastructure.

Purpose of the Study:

  • To develop a hybrid machine learning framework for rainfall prediction using satellite signal attenuation.
  • To transform satellite signal data into a reliable tool for meteorological applications.
  • To improve rainfall prediction accuracy in tropical climates with limited infrastructure.

Main Methods:

  • Utilized K-Means Clustering (k=4) with the Elbow Method to define four atmospheric regimes based on Signal-to-Noise Ratio (SNR) patterns.
  • Integrated unsupervised clustering with cluster-specific supervised Long Short-Term Memory (LSTM) deep learning models.
  • Employed a Software-Defined Radio (SDR) platform for data acquisition and preprocessing including SMOTE and standardization.

Main Results:

  • Cluster-specific LSTM models achieved R-squared values exceeding 0.92 across all identified atmospheric regimes.
  • Demonstrated superior performance of LSTM compared to Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) models.
  • Achieved high detection rates (Probability of Detection: 0.75-0.99) with low false alarms (False Alarm Ratio < 0.23).

Conclusions:

  • The hybrid machine learning framework effectively predicts rainfall by leveraging satellite signal attenuation.
  • The cluster-specific approach enhances prediction accuracy by accounting for diverse atmospheric dynamics.
  • Presents a scalable and effective solution for weather radar systems in tropical regions.