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Updated: Feb 13, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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Machine learning methods as a tool to analyse incomplete or irregularly sampled radon time series data.

M Janik1, P Bossew2, O Kurihara1

  • 1The National Institutes for Quantum and Radiological Science and Technology (QST), National Institute of Radiological Sciences (NIRS), 4-9-1 Anagawa, Inage-ku, 263-8555 Chiba, Japan.

The Science of the Total Environment
|March 21, 2018
PubMed
Summary

Machine learning effectively reconstructs incomplete indoor radon (Rn) time series data using environmental predictors like temperature and humidity. Gradient boosting machine models showed superior performance in filling data gaps and identifying key influencing variables.

Keywords:
EnvironmentLinear regressionMachine learningNeural networkRadonSensitivity analysis

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Area of Science:

  • Environmental Science
  • Data Science
  • Physics

Background:

  • Indoor radon (Rn) monitoring often suffers from incomplete or irregularly sampled time series data.
  • Accurate Rn concentration data is crucial for understanding exposure and health risks.
  • Complex environmental factors influence indoor radon levels, necessitating advanced modeling techniques.

Purpose of the Study:

  • To apply machine learning techniques for reconstructing incomplete indoor radon (Rn) time series.
  • To evaluate the performance of different machine learning algorithms against classical regression.
  • To identify the key environmental variables that predict indoor radon variability.

Main Methods:

  • Utilized machine learning algorithms: Random Forest, Gradient Boosting Machine, and Deep Learning.
  • Employed multiple regression within a generalized linear model for comparison.
  • Trained models on complete data sections to predict missing Rn values using environmental predictors.

Main Results:

  • Gradient Boosting Machine demonstrated superior performance in reconstructing Rn time series compared to other methods.
  • Machine learning successfully reconstructed and resampled missing Rn data when physical control data were available.
  • Temperature, relative humidity, and day of the year were identified as the most significant predictors for Rn concentration.

Conclusions:

  • Machine learning offers a robust solution for imputing missing indoor radon data.
  • Environmental variables, particularly temperature and humidity, are strong predictors of indoor radon variability.
  • The study validates the use of machine learning for enhancing environmental time series analysis.