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This study focuses on one-step ahead radon time-series forecasting using Long-Short Term Memory (LSTM). The predictor is based on a n-size sliding window on a multivariate time series containing soil radon data from the Phlegraean Fields area and various geophysical parameters. After evaluating a linear model as a base, using metrics such as mean absolute error and mean square error, we show that an LSTM model, which also exploits imputed information, exhibiting superior capabilities in capturing trends and peaks. This results shows better performance, including an improved Pearson’s correlation coefficient, compared previous studies.

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LSTM-Based Models for Radon Forecast

  • 章节

  • 发表日期：

    2026年

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  • Michele Di Giovanni,
  • Francesco A. N. Palmieri,
  • Giovanni Di Gennaro,
  • Amedeo Buonanno,
  • Fabrizio Ambrosino,
  • Mariagabriella Pugliese,
  • Giuseppe La Verde,
  • Carlo Sabbarese

摘要

This study focuses on one-step ahead radon time-series forecasting using Long-Short Term Memory (LSTM). The predictor is based on a n-size sliding window on a multivariate time series containing soil radon data from the Phlegraean Fields area and various geophysical parameters. After evaluating a linear model as a base, using metrics such as mean absolute error and mean square error, we show that an LSTM model, which also exploits imputed information, exhibiting superior capabilities in capturing trends and peaks. This results shows better performance, including an improved Pearson’s correlation coefficient, compared previous studies.