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Ambient dose rate variation in the Fukushima region visualized using explainable AI techniques.

Ryu Yoshida1, Hiroshi Kurikami2, Fumiya Nagao2

  • 1University of Aizu, Graduate Department of Computer and Information Systems, Tsuruga, Ikki-machi, Aizu-Wakamatsu City, Fukushima, 965-8580, Japan.

Journal of Environmental Radioactivity
|January 10, 2026
PubMed
Summary
This summary is machine-generated.

Ambient dose rates near Fukushima have declined since 2011, but reduction varies spatially. Forests show slower decline due to persistent contamination and topography, unlike urban areas. Explainable AI (XAI) helps understand these environmental radiation trends.

Keywords:
Ambient dose rate reductionArea-wide decontaminationExplainable AIGeospatial interpretationLand useLight gradient boosting machineUndisturbed forests

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Area of Science:

  • Environmental Science
  • Radiation Monitoring
  • Artificial Intelligence

Background:

  • The Fukushima Daiichi Nuclear Power Station accident in 2011 led to widespread radioactive material deposition.
  • Ambient dose rates have decreased due to decay and decontamination, but spatial variations persist, especially in forests.

Purpose of the Study:

  • To investigate long-term ambient dose rate changes using explainable AI (XAI).
  • To analyze temporal and spatial patterns of dose rate reduction over 12 years.
  • To identify factors influencing dose rate reduction, particularly in forested areas.

Main Methods:

  • Integrated dose rate mapping using diverse survey data (fixed-point, walk, carborne, airborne).
  • Development of a predictive model (Light Gradient Boosting Machine) for dose rate reduction ratios.
  • Application of SHapley Additive exPlanations (SHAP) for model interpretability and variable contribution analysis.

Main Results:

  • Land use significantly impacts dose rate reduction; urban and agricultural areas show faster declines than forests.
  • Topographical features (elevation, slope) influence trends in forests, with valleys and depressions exhibiting stagnation.
  • XAI effectively quantified variable contributions, revealing factors behind spatial variations in decontamination effectiveness.

Conclusions:

  • Forests exhibit slower ambient dose rate reduction compared to urban and agricultural areas due to persistent contamination and topographical influences.
  • Explainable AI is a valuable tool for analyzing environmental radiation data and validating decontamination effects.
  • The proposed geospatial approach supports informed policymaking for regional recovery and forest management post-accident.