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On Weather Data-Based Prediction of Gamma Exposure Rates Using Gradient Boosting Learning for Environmental Radiation

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This study predicts gamma radiation exposure rates using weather data. LightGBM machine learning model with data standardization accurately forecasts radiation levels, aiding environmental monitoring.

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Area of Science:

  • Environmental Science
  • Radiation Physics
  • Machine Learning

Background:

  • Gamma radiation is a known human carcinogen.
  • Observed correlations exist between weather data and gamma exposure rates.
  • Predicting gamma exposure could improve environmental radiation monitoring.

Purpose of the Study:

  • To investigate the predictability of gamma exposure rates using weather data.
  • To compare the performance of Long Short-Term Memory (LSTM) and Light Gradient Boosting Machine (LightGBM) algorithms.
  • To evaluate the effectiveness of data standardization versus normalization for preprocessing.

Main Methods:

  • Collected concurrent weather and gamma radiation data.
  • Employed Long Short-Term Memory (LSTM) and Light Gradient Boosting Machine (LightGBM) models.
  • Utilized data standardization and normalization preprocessing techniques.

Main Results:

  • Data standardization proved more effective than normalization, yielding smaller deviations.
  • LightGBM demonstrated superior prediction accuracy and faster processing times compared to LSTM.
  • The developed model can differentiate between weather-induced and actual radiation increases.

Conclusions:

  • Weather data can be utilized to predict gamma radiation exposure rates.
  • LightGBM with standardization is a highly effective method for this prediction task.
  • This approach enhances the capability of environmental radiation monitoring systems.