J. Nig. Soc. Phys. Sci. 3 (2021) 132–139

Journal of the
Nigerian Society

of Physical
Sciences

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

Based-Random Forest Regression

Omodele E. Olubia,b, Ebenezer O. Oniyaa,∗, Taoreed O. Owolabia

a Physics and Electronics Department, Adekunle Ajasin University, Akungba-Akoko, 342111, Ondo State, Nigeria.
b Achievers university, P.M.B 1030, Owo, Ondo State, Nigeria.

Abstract

This work develops predictive models for estimating radon (222Rn) activity concentration in the 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 perfor-
mance 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.

DOI:10.46481/jnsps.2021.177

Keywords: Radon, machine learning, meteorological parameters, atmosphere

Article History :
Received: 24 March 2021
Received in revised form: 15 April 2021
Accepted for publication: 24 April 2021
Published: 29 May 2021

©2021 Journal of the Nigerian Society of Physical Sciences. All rights reserved.
Communicated by: O. J. Abimbola

1. Introduction

The importance of radon (Rn-222), the only gaseous mem-
ber of the U-238 series, has been of interest to scientists since
the twentieth century when it was first suspected to be a caus
-ative agent for lung cancer among miners. The radioactive
gas has been a significant subject of research among health
and environmental scientists having been characterized as a

∗Corresponding author tel. no: +2348035033421
Email address: ebenezer.oniya@aaua.edu. (Ebenezer O. Oniya )

potential indoor source of air pollution. Its subsequent clas-
sification as a carcinogen has led to investigation and moni-
toring of the indoor concentration of the gas in several coun-
tries of the world [1-8]. The source of the noble gas is from
the decay of Ra-226 in bedrock and soil and migrates through
soil pores by gas-phase diffusion and advection to the surface
and its sink process is by radioactive decay [9, 10].

Due to some important characteristics of radon as a tracer
of atmospheric processes, there has been a growing interest
in recent decades in monitoring environmental radon. Being
a noble gas, it is not chemically reactive with other elements.

132



Olubi et al. / J. Nig. Soc. Phys. Sci. 3 (2021) 132–139 133

Its relative solubility in water and non-attachment to aerosols
makes it highly insusceptible to dry or wet atmospheric re-
moval processes. Its half-life of 3.82 days is comparable to the
life times of short-lived environmental pollutants (e.g NOx ,
SO2, CO, O3, CH4) and atmospheric residence times of water
and aerosols [10].

The noble gas has become very useful as a tracer of the
influence of the terrestrial environment on air mass composi-
tion. Some areas of application of ground-based radon obser-
vation include atmospheric, pollution studies and climatic
studies [11-16]. Observed anomalous behaviour of radon in
soil and groundwater during earthquake events has been em-
ployed as a precursor for impending earthquakes [17, 18].

Despite the progress that has been made in radon instru-
mentation, access to data on atmospheric radon concentra-
tion is still to a large extent, lacking in the public domain.
Africa for instance, has only one mention of a radon observa-
tory in the literature; an ANSTO-developed detector installed
at a Global Atmospheric watch (GAW ) station at Cape Point,
South Africa [10]. Ground based radon measurement meth-
ods have not been applied to study atmospheric processes
as have been done in Europe. As a matter of fact, the only
radon time series characterization to have been reported was
published recently for the first time on the continent [19]. In
the unavailability of measuring equipment, a theoretical ap-
proach to developing predictive models for radon concentra-
tion in the atmosphere may be a viable step in generating
synthetic data for the noble gas, using machine learning to
train available experimental data. Theoretical models have
been developed by several researchers in the literature to pre-
dict radon behaviour and concentration for various condi-
tions and applications [17, 18, 20-22]. Several studies in the
literature have reported the variation of atmospheric radon
and its progenies with changes in meteorological parameters
like temperature, pressure, humidity and windspeed [23-25].
[26] used these meteorological variables as independent pre-
dictors in the development of a multiple linear regression model
for estimation and prediction of the time series of radon and
thoron progeny concentrations.

Random forest (RF) methodology is a machine learning
technique developed by Leo Breiman and is useful for clas-
sification and prediction problems [27]. Its algorithm oper-
ates by sampling small divisions of the data, grows a tree pre-
dictor that is randomized on each small division, then aggre-
gates these predictors together. It applies bootstrap aggrega-
tion and random feature selection to individual classification
or regression trees for prediction [28]. Apart from the speed
and ease of implementation of random forests, their predic-
tions are remarkably accurate, with the ability to process a
very large input data whilst dealing with overfitting. They also
perform well with small to medium data [29]. Their good pre-
dictive abilities have made them highly applicable to regres-
sion and classification problems in the atmospheric sciences
[30-31].

The Grid Search (GS) is one of the algorithms for hyper-
parameter optimisation and tuning of models with an expec-
tation of the most accurate results. With a specified subset of

the hyperparameters space of the training algorithm, the al-
gorithm conducts a search with the aim of producing the best
combination of parameters yielding the most remarkable re-
sults. To apply a grid search, boundaries need to be specified
because some parameters within the algorithm’s parameter
space may contain unlimited values. The high dimensional
space problem with grid search algorithms is easily resolved
with parallelization of the of the process since the hyperpa-
rameters are usually not dependent on each other [32].

Multiple stepwise regression is efficient in the selection of
contributing factors used in establishing models that can do
statistical prediction. The critical objective it sets to achieve
is to discover the most cordial relationship between predictor
variables that would accurately forecast the predicted vari-
able. The regression process begins with the input of the mostly
contributing predictor variable to the prediction model. Ad-
ditional variables are continuously added as long as they are
of any essence to the regression equation. [33, 34].

This present work develops stepwise regression (SWR) and
grid search-based random forest regression (GS-RFR) mod-
els through which radon concentration can be estimated and
predicted using much more available meteorological param-
eters (air temperature (AT), atmospheric pressure (AP), ab-
solute humidity (AH), relative humidity (RH), wind direction
(WD) and wind speed (WS) as predictors. A comparison is
also made between both models in terms of performance.
The proposed model will help not only to predict radon con-
centration, it may also help to generate estimated or synthetic
radon data that can approximate measured data, for regions
that lack measuring instruments for atmospheric radon but
have access to meteorological data. It will also help estimate
radon data for sites where measurements have been inter-
rupted.

2. Theory

2.1. Description of Random Forest Regression

A random forest is described, according to [35], to consist
of N regression trees that are randomized also referred to as
a family. For any individual (i-th) tree, the predicted value at
the query point y can be represented as mn (x; Θi , Dn ), where
Θ1, . . . , ΘN are independent random variables that are not
dependent on Dn . Resampling of the training set is first done
using Θ before individual trees are grown. The finite forest
estimate for regression as a result of the combination of the
trees is

mN,n (x;Θ1, ...ΘN , Dn ) =
1

N

N∑
i =1

mn (x;Θi , Dn ) (1)

In the case of classification, the random forest classification
makes use of the majority vote among the classification trees.
The forest estimate for classification is

mN,n (x;Θ1, ...ΘN , Dn ) = {
1 if 1N

∑N
i =1 mn (x;Θi ,Dn )>

1
2

0 if otherwise
(2)

133



Olubi et al. / J. Nig. Soc. Phys. Sci. 3 (2021) 132–139 134

2.2. Description of Grid Search optimization

The implementation of the grid search technique involves
upper and lower bound vectors V = V1, V2, . . . , Vq and W =
W1, W2, . . . , Wq respectively, defined for each component of
hyperparameters H = H1, H2, . . . , Hq where q is the number
of hyperparameters. The optimization and parameter search
procedure involves taking n equally spaced points within the
search space over an interval of the form [Vi , Wi ] which in-
cludes of Vi and Wi . The algorithm searches through n

q pos-
sible points and a selection of the optimum values results, fol-
lowing the evaluation of each grid point in space [36].

2.3. Stepwise Regression

Based on the forward and backward selection, stepwise
regression is a self-determining process for in the selection of
independent variables. Multiple linear regression (MLR) has
the form

Y = βo +β1 X 1 +β2 X 2 +β3 X 3 ···+βp X p +ε (3)
In equation (3), Y is the output variable and X 1, X 2, X 3. . . are
predictor variables. βi are regression parameters, βo is an in-
tercept and ε is the random error term. The process is sum-
marised below;

1. If after the performance of simple multiple linear re-
gression of n predictor variables, all the variables show
remarkable significance, then the whole model contain-
ing all n variables is adopted.

2. If results show otherwise, simple n-variable linear re-
gression is performed with each of the predictor vari-
ables and the process selects the variable which gives
lowest p-value for t-test.

3. A subsequent n−1 variable regression is performed tak-
ing the selected variable in step 2 as common.

4. Step 3 is repeated with each significant variable becom-
ing added to the model in a stepwise manner. The test
for significance by stepwise regression can be applied
at two levels. The first being for addition of variables
and the second, for removal of variables [37].

2.4. Performance measuring parameters

Three performance measuring parameters were used to
assess the developed models namely correlation coefficient
(CC), root mean square error (RMSE), mean absolute error
(MAE). Correlation coefficient is defined as

C C =
∑N

i =1
(
Yi

∗ − Y ∗
) (

Yi − Y
)

√∑N
i =1

(
Yi ∗ − Y ∗

)2 √∑N
i =1

(
Yi − Y

)2 (4)
where where Yi

∗ and Yi are the mean values of the predicted
and actual outputs.
RMSE is defined as:

R M SE =
√√√√ 1

N

N∑
i =1

(Yi − Yi ∗)2 (5)

N represents the number of samples contained in the dataset
MAE is defined as:

M AE = 1
N

N∑
i =1

|Yi − Yi ∗| (6)

3. Methods

3.1. Description of sites

The data used in this work was published by [38], being
data measured in two Korea Antarctic Research Program sta-
tions namely King Sejong (KSG) and Jang Bogo ( JBS). Mea-
surements have been done jointly with the Australian Nuclear
Science and Technology Organisation (ANSTO). The Korea Po-
lar Research Institute (KOPRI) operates the KSG station (62.217◦
S, 58.783◦ W ). The station functions as a regional World Me-
teorological Organisation (WMO) Global Atmospheric Watch
(GAW ) station. The JBS is 10 m above sea level with coordi-
nates (74.623◦S, 164.228◦E). A detailed geographical descrip-
tion of the sites is seen in [39].

3.2. Radon and Meteorological Data

At JBS, radon measurements have been made using a 1200
L two-filter dual-flow-loop radon detector custom built by ANSTO.
Installed approximately 40 m east of the main station, air is
sampled at 55 L min−1 through 50 mm high-density polyethy-
lene pipe from approximately 6 m above ground level. In or-
der to avoid thoron (220Rn; t0.5 = 55.6 s) from entering into
the pipe and contaminating sampled air, a 400 L delay vol-
ume is coupled within the sampling line. At approximately
170 m from the radon detector, meteorological data was col-
lected from a 10 m tower with instrumentation composed of a
sonic anemometer, temperature and humidity probe, barom-
eter and a windspeed logger. In post processing, all observa-
tions are aggregated to hourly values [39]. A radon detector
similar in operation to that in JBS but with a volume of 1500 L
was used for radon data collection in KSG with meteorologi-
cal data collected from a nearby observation system [40]. The
dataset used was measured between December and February
with 1818 and 1955 datapoints for JBS and KSG respectively.

Table 1 shows the statistical analysis from JBS and KSG.
The mean, standard deviation and range are presented. While
the mean and range describe the content of the dataset, the
correlation coefficients depict the level of linear relationship
between the target and predictor variables. Both tables in-
dicate weak correlation between AR n and the descriptors in-
dicating that a purely linear regression model may be insuf-
ficient to represent the relationship between the descriptors
and target.

3.3. Computational methodology

3.3.1. SWR model
A flow chart of the stepwise process is presented in figure

1. Whenever a variable x is added in each step, all the predic-
tor variables in the model are assessed for their significance
p. If it has been reduced below a specified threshold.

134



Olubi et al. / J. Nig. Soc. Phys. Sci. 3 (2021) 132–139 135

Table 1: Statistical analysis of dataset from JBS

Correlation Coefficient Mean Standard Deviation Range
AR n (Bq/m

3) 1 0.937 0.743 5.213
WS (m/s) -0.32 3.723 3.284 17.600
WD (o ) -0.27 167.766 112.667 359.700
AT (o C) 0.22 -3.055 3.814 19.300
RH (%) -0.11 57.524 16.121 72.400
AP (hPa) -0.16 982.704 7.370 36.700
AH (g/m3) 0.073 2.317 0.822 4.05

Table 2: Statistical analysis of dataset from KSG

Correlation Coefficient Mean Standard Deviation Range
AR n (Bq/m

3) 1 63.344 30.921 314.060
WS (m/s) -0.05 7.043 3.463 18.600
WD (o ) -0.09 235.993 96.123 358.500
AT (o C) 0.40 -0.023 1.447 11.600
RH (%) 0.12 84.583 8.763 41.200
AP (hPa) -0.02 987.753 9.369 43.000
AH (g/m3) 0.39 4.111 4.111 4.070

Figure 1: The stepwise regression flow chart

3.4. Building of GS-RFR model

The computation of the GS-RFR model development was
achieved using PYTHON software. The radon concentration
and the descriptors, which include (air temperature (AT), at-
mospheric pressure (AP), absolute humidity (AH), relative hu-
midity (RH), wind direction (WD) and wind speed (WS), after
randomization, was partitioned into training and testing sets
in the ratio of 8:2 respectively. The RFR model development
was done with the training set, while the general predictive
ability of the model was assessed using the 20% test set. A
helpful purpose for randomization is that it enhances com-
putation efficiency by ensuring unbiased spread of data dur-
ing both the training or testing phase. The performance al-
gorithm was optimized through an optimum selection of hy-
perparameters using grid search (GS) with cross validation.
Table 3 below shows the hyperparameters that were tuned as
suggested in the literature [41, 42]. During the hyperparame-
ters tuning, the 5-fold cross validation was used as the fitness
function. Verification of the RF model with the optimum hy-

perparameters was carried out on the testing set.
*

4. 4. Results and Discussion

4.1. Comparison of Performance between the SWR and GS-
RFR

For the two datasets, the performance of the two models
developed by SWR and GS-RFR is depicted in figure 1. The
predictive capabilities of the two models were assessed using
the performance measuring parameters: correlation coeffi-
cient (CC), root mean square error (RMSE) and mean abso-
lute error (MAE).

Tables 4 and 5 shows the estimated predictive performance
for the two regression methods based on correlation coeffi-
cient, root mean square error and mean absolute error. Fig-
ure 2 compares the performance of the test set on the models
developed using the data from KSG and JBS. The figures show
better performance by the GS-RFR model over the more tra-
ditional SWR. Considering RMSE, an improvement of 64.09
% and 15.19 % was obtained on the training and test datasets,
respectively at KSG while at JBS, an improvement of 75.04 %
and 28.04 % was obtained on the training and test datasets,
respectively (Table 6). The optimum hyperparameters for the
RFR algorithm for each dataset is summarized in Table 7.

Table 4. Predictive performance of the two regression mod-
els in terms of Correlation Coefficient (CC), Root Mean Square
Error (RMSE) and Mean Absolute Error (MAE) for KSJ dataset.

The GS-RFR model presents the smallest RMSE on the two
datasets employed. It also achieved the highest correlation
coefficient on both training and test sets. The plots in figure 3
show the correlation between predicted and measured values
of radon concentration. It can be seen that the GS-RFR model

135



Olubi et al. / J. Nig. Soc. Phys. Sci. 3 (2021) 132–139 136

Table 3: Hyperparameters description

No Hyperparameters Definition
1 Max_depth The maximum depth of Decision trees (DT).
2 Min_samples_split The minimum number of samples for the split
3 Min_samples_leaf The minimum number of samples at the leaf node
4 n_estimators The number of trees in the forest of the model
5 Max_features The number of features considered during the selection of the best splitting

Table 4: Predictive performance of the two regression models in terms of Correlation Coefficient (CC), Root Mean Square Error (RMSE) and Mean Absolute
Error (MAE) for KSJ dataset

Method CC RMSE (Bq/m3) MAE (Bq/m3)
Training Test Training Test Training Test

GS-RFR 0.99 0.83 8.57 20.26 5.38 14.07
SWR 0.64 0.61 23.86 23.89 0.01 0.10

(a)

(b)

(c)

(d)

Figure 2: Performance comparison between the developed models
for training (TR) and test (TS) sets on the basis of RMSE on (a) KSG
training dataset (b) KSG test dataset (c) JBS training dataset (d) JBS
test dataset

made a potential success in describing the non-linear rela-
tionship between atmospheric radon concentration and in-
fluencing meteorological parameters considering strong cor-
relation coefficients it achieved.

5. Conclusion

In this work, modelling of atmospheric radon as done us-
ing the more traditional stepwise regression (SWR) and a novel

136



Olubi et al. / J. Nig. Soc. Phys. Sci. 3 (2021) 132–139 137

Table 5: Predictive performance of the two regression models in terms of Correlation Coefficient (CC), Root Mean Square Error (RMSE) and Mean Absolute
Error (MAE) for KSJ dataset

Method CC RMSE (Bq/m3) MAE (Bq/m3)
Training Test Training Test Training Test

GS-RFR 0.98 0.82 0.17 0.46 0.11 0.32
SWR 0.61 0.66 0.66 0.63 0.06 6553

Table 6: Improvement of GS-RFR over SWR in this study

JBS KSG
Training set Test set Training set Test set
75.04 % 28.04 % 64.09 % 15.19 %

Table 7: Selected optimum hyperparameters after the grid search

Hyperparameters JBS KSG
Max_depth 2000 2000
Min_samples_split 2 3
Min_samples_leaf 1 1
n_estimators 650 50
Max_features 3 3

(a)

(b)

(c)

(d)

Figure 3: Performance comparison between the developed models
for training (TR) and test (TS) sets on the basis of RMSE on (a) KSG
training dataset (b) KSG test dataset (c) JBS training dataset (d) JBS
test dataset.

137



Olubi et al. / J. Nig. Soc. Phys. Sci. 3 (2021) 132–139 138

grid search based random forest regression (GS-RFR). Datasets
from two radon stations in Antarctica were used in the build-
ing of the models. Important factors such as air temperature,
atmospheric pressure, absolute humidity, relative humidity,
wind direction and wind speed were used as predictors. Com-
paring both models, the results show that the GS-RFR model
performed better on both datasets in the training and test-
ing phases. It presents a respective training and test improve-
ment of 64.09 % and 15.19 % on one dataset and 75.04 % and
28.04 % on the other. Atmospheric radon data, which is find-
ing more relevance today in the atmospheric sciences, is still
scarce and not readily available. The precision and robust-
ness of the developed models would be of significant inter-
est in determining the concentration of radon (222Rn) activ-
ity concentration in the atmosphere for various physical ap-
plications especially in regions where field measuring equip-
ment for radon is not available but have meteorological pa-
rameters are.

Acknowledgment

The Korean Polar Research Institute is acknowledged for
making the employed radon and meteorological data avail-
able online. We thank the referees for the positive enlighten-
ing comments and suggestions, which have greatly helped us
in making improvements to this paper.

References

[1] O. S. Ajayi, E. O. Owoola, O. E. Olubi & C. G. Dike, “Sur-
vey of indoor radon levels in some universities in southwest-
ern Nigeria”, Radiation Protection Dosimetry 87 (2019) 34.
https://doi.org/10.1080/15275922.2016.1230909

[2] 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.
https://doi.org/10.1016/j.jenvrad.2016.01.018

[3] C. Grossi, A. Àgueda, F. R. Vogel, A. Vargas, M. Zimnoch, P. Wach, J.
E. Martín, I. López-Coto, J. P. Bolívar, J. A. Morguí & X. Rodó, “Analy-
sis of ground-based 222Rn measurements over Spain: Filling the gap
in southwestern Europe”, Journal of Geophysical Research 121 (2016)
11,021. https://doi.org/10.1002/2016JD025196

[4] I. Lázár, E. Tóth, G. Marx, I. Cziegler & G. J. Köteles, “Effects of residen-
tial radon on cancer incidence”, Journal of Radioanalytical and Nuclear
Chemistry, 258 (2003) 519.

[5] A. M. Maghraby, K. Alzimami & M. Abo-Elmagd, “Estimation
of the residential radon levels and the population annual ef-
fective dose in dwellings of Al-kharj, Saudi Arabia”, Journal
of Radiation Research and Applied Sciences, 7 (2014) 577.
https://doi.org/10.1016/j.jrras.2014.09.013

[6] 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. https://doi.org/10.1111/j.1539-
6924.2012.01768.x

[7] V. T. Rasmussen, “Determining the mean year value of radon in
the indoor air”, MATEC Web of Conferences, 282 (2019) 02001.
https://doi.org/10.1051/matecconf/201928202001

[8] K. Walczak, J. Olszewski, P. Politański & M. Zmyślony, “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.
https://doi.org/10.13075/ijomeh.1896.00987

[9] F. Giustini, G. Ciotoli, A. Rinaldini, L. Ruggiero & M. Voltaggio, “Map-
ping the geogenic radon potential and radon risk by using Empiri-
cal Bayesian Kriging regression: A case study from a volcanic area
of central Italy”, Science of The Total Environment, 661 (2019) 449.
https://doi.org/10.1016/j.scitotenv.2019.01.146

[10] W. Zahorowski, S. D. Chambers & A. Henderson-Sellers, “Ground
based radon-222 observations and their application to atmo-
spheric studies” Journal of Environmental Radioactivity 76 (2004)
3. https://doi.org/10.1016/j.jenvrad.2004.03.033

[11] S. D.Chambers, D. Scott, W. Zahorowski, A. G. Williams, J. Craw-
ford & 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.
https://doi.org/10.1029/2012JD018212

[12] 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. https://doi.org/10.1007/s10967-006-0458-1

[13] 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.
https://doi.org/10.5194/acp-14-12763-2014

[14] A. Podstawczyńska & S. D. Chambers, “Radon-based technique for the
analysis of atmospheric stability – a case study from Central Poland”,
Nukleonika 63 (2018) 47. https://doi.org/10.2478/nuka-2018-0006

[15] 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.
https://doi.org/10.1016/j.atmosenv.2018.10.020

[16] 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.
https://doi.org/10.3402/tellusb.v68.30967

[17] A. Pasini, R. Salzano & A. Attanasio, “Modeling Radon Behav-
ior for Characterizing and Forecasting Geophysical Variables at the
Atmosphere–Soil Interface”, In: Sengupta D. (eds) Recent Trends in
Modelling of Environmental Contaminants, Springer, New Delhi,2014.
https://doi.org/10.1007/978-81-322-1783-1_9

[18] B. Zmazek, L. Todorovski, S. Džeroski, J. Vaupotič & I. Kobal, “Appli-
cation of decision trees to the analysis of soil radon data for earth-
quake prediction”, Applied Radiation and Isotopes 58 (2003) 697.
https://doi.org/10.1016/S0969-8043(??)00094-0

[19] 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.
https://doi.org/10.1016/j.atmosenv.2017.12.010

[20] K. M. Ajayi, K. Shahbazi, P. Tukkaraja & K. Katzenstein, "A dis-
crete model for prediction of radon flux from fractured rocks", Jour-
nal of Rock Mechanics and Geotechnical Engineering 10 (2018) 879.
https://doi.org/10.1016/j.jrmge.2018.02.009

[21] 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.
https://doi.org/10.29195/iascs.01.01.0025

[22] 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.
https://doi.org/10.1029/2002GL016726

[23] 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. https://doi.org/10.1515/nuka-2016-
0049

[24] G. Mentes & I. Eper-Pápai, "Investigation of temperature and
barometric pressure variation effects on radon concentra-
tion in the Sopronbánfalva Geodynamic Observatory, Hun-
gary" Journal of Environmental Radioactivity, 149 (2016) 64.

138



Olubi et al. / J. Nig. Soc. Phys. Sci. 3 (2021) 132–139 139

https://doi.org/10.1016/j.jenvrad.2015.07.015
[25] 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. https://doi.org/10.1016/j.radmeas.2004.06.015

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

[27] L. Breiman, "Random forests", Machine Learning 45 (2001)5.
https://doi.org/10.1201/9780429469275-8

[28] R. Xu, Improvements to random forest methodology Disser-
tation (Doctor of Philosophy) Iowa State University, 2013.
http://lib.dr.iastate.edu/etd/13052/

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

[30] A. C. Keller & J. M. Evans, "Application of random forest regression to
the calculation of gas-phase chemistry within the GEOS-Chem chem-
istry model v10", Geoscientific Model Development 12 (2019) 1209.
https://doi.org/10.5194/gmd-12-1209-2019

[31] A. Masih, "Application of Random Forest Algorithm to Predict the At-
mospheric Concentration of NO2", 2019 Ural Symposium on Biomed-
ical Engineering, Radioelectronics and Information Technology (US-
BEREIT), 252–255.

[32] P. Liashchynskyi & P. Liashchynskyi, "Grid Search, Random Search, Ge-
netic Algorithm: A Big Comparison for NAS", ArXiv (2019)1–11.

[33] T. Gao & L. Xie, Multivariate regression analysis and statistical modeling
for summer extreme precipitation over the Yangtze River basin, China,
Advances in Meteorology, 2014. https://doi.org/10.1155/2014/269059

[34] 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. https://doi.org/10.4209/aaqr.2011.11.0220

[35] G. Biau & E. Scornet, "A random forest guided tour", Test 25 (2016)197.
https://doi.org/10.1007/s11749-016-0481-7

[36] 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 .
https://doi.org/10.1063/5.0024595.

[37] R. Silhavy, P. Silhavy & Z. Prokopova, "Evaluating subset selection meth-
ods for use case points estimation", Information and Software Technol-
ogy 97 (2018)1. https://doi.org/10.1016/j.infsof.2017.12.009

[38] S. Hong, Radon 222 and meteorological time series at Jang Bogo
and King Sejong Station, Antarctica, in 2015-2016, Pangae, 2017.
https://doi.org/https://doi.org/10.1594/PANGAEA.879451.

[39] 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: At-
mospheres, 122(2017)13525. https://doi.org/10.1002/2017JD026833.

[40] 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. https://doi.org/10.5194/acp-14-
9903.

[41] 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 com-
puting model using random forest and particle swarm optimization for
estimation of undrained shear strength of soil", Sustainability (Switzer-
land) 12 (2020) 1. https://doi.org/10.3390/su12062218.

[42] C. Qi, Q. Chen, A. Fourie & Q. Zhang, "An intelligent
modelling framework for mechanical properties of ce-
mented paste backfill", Minerals Engineering 123 (2018) 16.
https://doi.org/10.1016/j.mineng.2018.04.010

139