Development of Predictive Model for Radon-222 Estimation in the Atmosphere using Stepwise Regression and Grid Search Based-Random Forest Regression


  • Omodele Olubi Physics and Electronics Department, Adekunle Ajasin University, Akungba-Akoko, 342111, Ondo State, Nigeria; Achievers university, P.M.B 1030, Owo, Ondo State, Nigeria.
  • Ebeneze Oniya Physics and Electronics Department, Adekunle Ajasin University, Akungba-Akoko, 342111, Ondo State, Nigeria.
  • Taoreed Owolabi Physics and Electronics Department, Adekunle Ajasin University, Akungba-Akoko, 342111, Ondo State, Nigeria.


radon, machine learning, meteorological parameters, atmosphere


This work develops predictive models for estimating radon (222Rn) activity concentration in the atmosphere using novel grid search based random forest regression (GS-RFR) and stepwise regression (SWR). The developed models employ meteorological parameters which include the temperature, pressure, relative and absolute humidity, wind speed and wind direction as descriptors.  Experimental data of radon concentration and meteorological parameters from two observatories of the Korea Polar Research Institute in Antarctica (King Sejong and Jang Bogo) have been employed in this work.  The performance of the developed models was assessed using three different performance measuring parameters. On the basis of root mean square error (RMSE), the GS-RFR shows better performance over the SWR. An improvement of 64.09 % and 15.19 % was obtained on the training and test datasets, respectively at King Sejong station. At the Jang Bogo station, an improvement of 75.04 % and 28.04 % was obtained on the training and test datasets, respectively. The precision and robustness of the developed models would be of significant interest in determining the concentration of radon (222Rn) activity concentration in the atmosphere for various physical applications especially in regions where field measuring equipment for radon is not available or measurements have been interrupted.


O. S. Ajayi, E. O. Owoola, O. E. Olubi & C. G. Dike, ``Survey of indoor radon levels in some universities in southwestern Nigeria'', Radiation Protection Dosimetry 87 (2019) 34.

J. Chen & K. L. Ford, ``A study on the correlation between soil radon potential and average indoor radon potential in Canadian cities'', Journal of Environmental Radioactivity, 166 (2017) 152.

C. Grossi, A. Agueda, F. R. Vogel, A. Vargas, M. Zimnoch, P. Wach, J. E. Martin, I. Lopez-Coto, J. P. Bolivar, J. A. Morgui & X. Rodo, ``Analysis of ground-based 222Rn measurements over Spain: Filling the gap in southwestern Europe'', Journal of Geophysical Research 121 (2016) 11,021.

I. Lazar, E. Toth, G. Marx, I. Cziegler & G. J. K"{oteles, ``Effects of residential radon on cancer incidence'', Journal of Radioanalytical and Nuclear Chemistry, 258 (2003) 519.

A. M. Maghraby, K. Alzimami & M. Abo-Elmagd, ``Estimation of the residential radon levels and the population annual effective dose in dwellings of Al-kharj, Saudi Arabia'', Journal of Radiation Research and Applied Sciences, 7 (2014) 577.

W. J. Mccarthy, R. Meza, J. Jeon & S. H. Moolgavkar, ``Chapter 6: Lung cancer in never smokers: Epidemiology and risk prediction models'', Risk Analysis 32 (2012) 69.

V. T. Rasmussen, ``Determining the mean year value of radon in the indoor air'', MATEC Web of Conferences, 282 (2019) 02001.

K. Walczak, J. Olszewski, P. Politanski & M. Zmyslony, ``Occupational exposure to radon for underground tourist routes in Poland: doses to lung and the risk of developing lung cancer'', International Journal of Occupational Medicine and Environmental Health, 30 (2017) 687.

F. Giustini, G. Ciotoli, A. Rinaldini, L. Ruggiero & M. Voltaggio, ``Mapping the geogenic radon potential and radon risk by using Empirical Bayesian Kriging regression: A case study from a volcanic area of central Italy'', Science of The Total Environment, 661 (2019) 449.

W. Zahorowski, S. D. Chambers & A. Henderson-Sellers, ``Ground based radon-222 observations and their application to atmospheric studies'' Journal of Environmental Radioactivity 76 (2004) 3.

S. D.Chambers, D. Scott, W. Zahorowski, A. G. Williams, J. Crawford & A. D. Griffiths, ``Identifying tropospheric baseline air masses at Mauna Loa Observatory between 2004 and 2010 using Radon-222 and back trajectories: Radon-Derived Mauna Loa Baseline Events'', Journal of Geophysical Research: Atmospheres 118 (2013) 992.

D. Desideri, C. Roselli, L. Feduzi & M. Assunta Meli, ``Monitoring the atmospheric stability by using radon concentration measurements: A study in a Central Italy site'', Journal of Radioanalytical and Nuclear Chemistry, 270 (2006) 523.

A. D. Griffiths, F. Conen, E. Weingartner, L. Zimmermann, S. D. Chambers, A. G. Williams & M. Steinbacher, ``Surface-to-mountaintop transport characterised by radon observations at the Jungfraujoch'', Atmospheric Chemistry and Physics 14 (2014) 12763.

A. Podstawczynska & S. D. Chambers, ``Radon-based technique for the analysis of atmospheric stability -- a case study from Central Poland'', Nukleonika 63 (2018) 47.

R. Vecchi, F. A. Piziali, G. Valli, M. Favaron & V. Bernardoni, ``Radon-based estimates of equivalent mixing layer heights: a long-term assessment'', Atmospheric Environment 197 (2019) 150.

A. G. Williams, S. D. Chambers, F. Conen, S. Reimann, M. Hill, A. D. Griffiths & J. Crawford, ``Radon as a tracer of atmospheric influences on traffic-related air pollution in a small inland city'', Tellus B: Chemical and Physical Meteorology, 68 (2016) 30967.

A. Pasini, R. Salzano & A. Attanasio, ``Modeling Radon Behavior for Characterizing and Forecasting Geophysical Variables at the Atmosphere--Soil Interface'', In: textit{Sengupta D. (eds) Recent Trends in Modelling of Environmental Contaminants, Springer, New Delhi,2014.

B. Zmazek, L. Todorovski, S. Dvzeroski, J. Vaupotiv{c & I. Kobal, ``Application of decision trees to the analysis of soil radon data for earthquake prediction'', Applied Radiation and Isotopes 58 (2003) 697.

R. Botha, C. Labuschagne, A. G. Williams, G. Bosman, E. G. Brunke, A. Rossouw & R. Lindsay, ``Characterising fifteen years of continuous atmospheric radon activity observations at Cape Point (South Africa)'', Atmospheric Environment, 176 (2018) 30.

K. M. Ajayi, K. Shahbazi, P. Tukkaraja & K. Katzenstein, "A discrete model for prediction of radon flux from fractured rocks", Journal of Rock Mechanics and Geotechnical Engineering 10 (2018) 879.

A. V. Glushkov, O. Yu Khetselius, V. V. Buyadzhi, Y. V. Dubrovskaya, I. N. Serga, E. V. Agayar & V. B. Ternovsky, "Nonlinear chaos-dynamical approach to analysis of atmospheric radon 222Rn concentration time series", Indian Academy of Sciences -- Conference Series 1 (2017) 61.

A. Pasini & F. Ameli, "Radon short range forecasting through time series preprocessing and neural network modeling: Radon Short Range Forecasting", Geophysical Research Letters 30 (2003) 1.

M. Janik & P. Bossew, "Analysis of simultaneous time series of indoor, outdoor and soil air radon concentrations, meteorological and seismic data", Nukleonika 61 (2016) 295.

G. Mentes & I. Eper-Papai, "Investigation of temperature and barometric pressure variation effects on radon concentration in the Sopronbanfalva Geodynamic Observatory, Hungary" Journal of Environmental Radioactivity, 149 (2016) 64.

K. Singh, M. Singh, S. Singh, H. S. Sahota & Z. Papp, "Variation of radon (Rn) progeny concentrations in outdoor air as a function of time, temperature and relative humidity", Radiation Measurements 39 (2005)213.

F. Simion, V. Cuculeanu, E. Simion & A. Geicu, "Modeling the 222Rn and 220Rn progeny concentrations in atmosphere using multiple linear regression with meteorological variables as predictors", Romanian Reports in Physics 65 (2013) 524.

L. Breiman, "Random forests", Machine Learning 45 (2001)5.

R. Xu, {it Improvements to random forest methodology Dissertation (Doctor of Philosophy) Iowa State University, 2013.

G. Biau, "Analysis of a Random Forests Model", Journal of Machine Learning Research, 13 (2012) 1063.

A. C. Keller & J. M. Evans, "Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10", Geoscientific Model Development 12 (2019) 1209.

A. Masih, "Application of Random Forest Algorithm to Predict the Atmospheric Concentration of NO2", 2019 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), 252--255.

P. Liashchynskyi & P. Liashchynskyi, "Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS", ArXiv (2019)1--11.

T. Gao & L. Xie, textit{Multivariate regression analysis and statistical modeling for summer extreme precipitation over the Yangtze River basin, China, Advances in Meteorology, 2014.

M. Lin, J. Tao, C. Y. Chan, J. J. Cao, Z. S. Zhang, L. H. Zhu & R. J. Zhang, "Regression analyses between recent air quality and visibility changes in megacities at four haze regions in china", Aerosol and Air Quality Research 12 (2012) 1049.

G. Biau & E. Scornet, "A random forest guided tour", Test 25 (2016)197.

S. M. I. Shamsah & T. O. Owolabi, "Empirical method for modeling crystal lattice parameters of A2XY6 cubic crystals using grid search-based extreme learning machine", Phys. J. Appl 128 (2020) 185106 .

R. Silhavy, P. Silhavy & Z. Prokopova, "Evaluating subset selection methods for use case points estimation", Information and Software Technology 97 (2018)1.

S. Hong, textit{Radon 222 and meteorological time series at Jang Bogo and King Sejong Station, Antarctica, in 2015-2016, Pangae, 2017.

S. D. Chambers, T. Choi, S. J. Park, A. G. Williams, S. B. Hong, L. Tositti, A. D. Griffiths, J. Crawford & E. Pereira, "Investigating Local and Remote Terrestrial Influence on Air Masses at Contrasting Antarctic Sites Using Radon-222 and Back Trajectories", Journal of Geophysical Research: Atmospheres, 122(2017)13525.

S. D. Chambers, S. B. Hong, A. G. Williams, J. Crawford, A. D. Griffiths & S. J. Park, "Characterising terrestrial influences on Antarctic air masses using Radon-222 measurements at King George Island", Atmospheric Chemistry and Physics 14 (2014) 9903.

B. T. Pham, C. Qi, L. S. Ho, T. Nguyen-Thoi, N. Al-Ansari, M. D. Nguyen, H. D. Nguyen, H. B. Ly, H. Van Le & I. Prakash, "A novel hybrid soft computing model using random forest and particle swarm optimization for estimation of undrained shear strength of soil", Sustainability (Switzerland) 12 (2020) 1.

C. Qi, Q. Chen, A. Fourie & Q. Zhang, "An intelligent modelling framework for mechanical properties of cemented paste backfill", Minerals Engineering 123 (2018) 16.



How to Cite

Development of Predictive Model for Radon-222 Estimation in the Atmosphere using Stepwise Regression and Grid Search Based-Random Forest Regression. (2021). Journal of the Nigerian Society of Physical Sciences, 3(2), 132-139.



Original Research

How to Cite

Development of Predictive Model for Radon-222 Estimation in the Atmosphere using Stepwise Regression and Grid Search Based-Random Forest Regression. (2021). Journal of the Nigerian Society of Physical Sciences, 3(2), 132-139.