@article{oai:repo.qst.go.jp:00080365,
 author = {Hosoda, M. and Tokonami, S. and Suzuki, T. and Janik, Miroslaw and Janik, Miroslaw},
 journal = {Radiation Measurements},
 month = {Aug},
 note = {Interest in radon (Rn) is not limited only to its impact on health and its dose to the public, but due to its properties, the techniques to analyse its behavior can be used in many fields such as radiotherapy, atmospheric physics, geophysics, geohazards, mineral exploration, and even planetary science.

Nowadays machine learning methods provide extremely important tools for intelligent environmental data analysis, processing and visualization.
We describe application of machine learning to environmental sciences with an emphasis on the radon exhalation rate in order to express responses from multivariable time-series data collected at a measuring site near the Sakurajima volcano (Kagoshima, Japan).},
 title = {Machine learning as a tool for analysing the impact of environmental parameters on the radon exhalation rate from soil},
 volume = {138},
 year = {2020}
}