Machine learning analysis of 137Cs contamination of terrestrial plants after the Fukushima accident using the random forest algorithm. (January 2022)

• Record Type: Journal Article
• Authors: Shuryak, Igor
• Is Part Of: Journal of environmental radioactivity. Volume 241(2022)
• Publication Date: 2022-01
• Subjects: Radioactive contamination -- 137Cs -- Radionuclides -- Plants -- Trees -- Modeling -- Machine learning -- Random forests; Radioactivity -- Periodicals; Radiation, Background -- Periodicals; Radioecology -- Periodicals; Radioactive pollution -- Periodicals; Environmental Pollutants -- Periodicals; Radioactive Pollutants -- Periodicals; Radioactivity -- Periodicals; Radioécologie -- Périodiques; Pollution radioactive -- Périodiques; Fond de rayonnement -- Périodiques; 539.752
• DOI: 10.1016/j.jenvrad.2021.106772
• Languages: English
• ISSNs: 0265-931X

Abstract

Abstract: Radioactive contamination of terrestrial plants was extensively investigated and quantitatively modeled after the Fukushima nuclear power plant accident. This phenomenon, which is important for ecosystem functioning and protection of human health, is influenced by multiple factors, including plant species, time after the accident, and climate. Machine learning algorithms such as random forests (RF) have a record of strong performance on large multi-dimensional data sets, but, to our knowledge, combined data on post-Fukushima plant contamination with radionuclides were not yet subjected to a machine learning analysis. Here we performed such analysis on two large published data sets: (1) 137 Cs activity concentrations in four common Japanese forest tree species. (2) Plant/soil 137 Cs concentration ratios in multiple perennial plant species. The goal was to show the usefulness of machine learning for identifying and quantifying the main trends of 137 Cs contamination in terrestrial plants. Each data set was split randomly into training and testing parts, RF was fitted and tuned on the training parts, and its performance was assessed on the testing parts by three metrics: coefficient of determination (R 2 ), root mean squared error, and mean absolute error. Synthetic noise variables and the Boruta algorithm were used in a customized procedure to identify the most important predictor variables, which consistently outperformed random noise. Good agreement between observations and RF predictions ( e.g . R 2 ∼0.9 on testing data) was obtained on both data sets. The effects of the most important predictors ( e.g. time after the accident, 137 Cs land contamination level, and plant species) and interactions between them were quantified by partial dependence plots. These results of machine learning analyses of large data collections can help to complement previous modeling efforts, and to clarify the patterns of 137 Cs contamination of plants after the Fukushima accident. Highlights: Used a machine learning algorithm to analyze 137 Cs contamination of terrestrial plants after the Fukushima accident. Described and visualized the main patterns of 137 Cs contamination in various plant species. Identified the most important predictors for these patterns. Generated numerical predictions for the mean contamination level, and also for various percentiles of its distribution.