Applying Machine Learning to Weather and Pollution Data Analysis for
a Better Management of Local Areas: The Case of Napoli, Italy
Lelio Campanile
, Pasquale Cantiello
, Mauro Iacono
, Roberta Lotito
Fiammetta Marulli
and Michele Mastroianni
Dipartimento di Matematica e Fisica, Universit
a degli Studi della Campania ”L. Vanvitelli”, viale Lincoln 5, Caserta, Italy
Osservatorio Vesuviano, Istituto Nazionale di Geofisica e Vulcanologia, via Diocleziano 328, Napoli, Italy
Air Quality, Forecasting, Machine Learning, Regression, Data Analysis, Campania.
Local pollution is a problem that affects urban areas and has effects on the quality of life and on health
conditions. In order to not develop strict measures and to better manage territories, the national authorities
have applied a vast range of predictive models. Actually, the application of machine learning has been studied
in the last decades in various cases with various declination to simplify this problem. In this paper, we apply
a regression-based analysis technique to a dataset containing official historical local pollution and weather
data to look for criteria that allow forecasting critical conditions. The methods was applied to the case study
of Napoli, Italy, where the local environmental protection agency manages a set of fixed monitoring stations
where both chemical and meteorological data are recorded. The joining of the two raw dataset was overcome
by the use of a maximum inclusion strategy as performing the joining action with ”outer” mode. Among the
four different regression models applied, namely the Linear Regression Model calculated with Ordinary Least
Square (LN-OLS), the Ridge regression Model (Ridge), the Lasso Model (Lasso) and Supervised Nearest
Neighbors Regression (KNN), the Ridge regression model was found to better perform with an R
of Determination) value equal to 0.77 and low value for both MAE (Mean Absolute Error) and MSE (Mean
Squared Error), equal to 0.12 and 0.04 respectively.
Since the beginning of the Industrial Revolution, one
of the most affected environmental sector is the air:
indeed, exhausted gases from human activities have
changed the local atmosphere composition and this
variation led to a relationship change between human
and local area. It is an instilled thought that near in-
dustrial area or even in high urbanization area the air
quality is poor, while, in order to ’breathe fresh air’,
it is necessary to go to a natural place like seafront
or mountains. As matter of fact, the connection be-
tween the presence of some compounds, their con-
centration and the onset of specific disease is widely
studied. The direct consequences is the different use
of the territory with economic implication.
The natural atmospheric composition is well
known: nitrogen accounts for 78%, oxygen 21% and
argon 0.9%. Gases like carbon dioxide, nitrous ox-
ides, methane, and ozone are trace compounds that
account for about a tenth of one percent of the atmo-
sphere. The presence and concentration of the trace
gases can be characteristic for particular area (vol-
canic areas with the presence of hydrogen sulphide,
rice paddies with methane presence): the variation
of these compounds, both qualitatively and quantita-
tively, from their standard presence is called pollu-
tion. Actually, in consideration of the human activi-
ties and aerial transport/dispersion, it is nearly impos-
sible to find a location on Hearth unpolluted. How-
ever, it is clear that the pollution from a city center is
different compared both to an industrial area and to
a local mountain village. In addition, even between
two similar cities, the atmospheric pollution can be
completely different due to meteorological asset, city
architecture, regional location. Generally, we can say
that the air quality depends on two classes of influ-
ence: the first regards the natural condition such as
local weather, presence of water body, presence of
emitting geological structures and so on. The sec-
ond class regards the human-effect presence and so
Campanile, L., Cantiello, P., Iacono, M., Lotito, R., Marulli, F. and Mastroianni, M.
Applying Machine Learning to Weather and Pollution Data Analysis for a Better Management of Local Areas: The Case of Napoli, Italy.
DOI: 10.5220/0010540003540363
In Proceedings of the 6th International Conference on Internet of Things, Big Data and Security (IoTBDS 2021), pages 354-363
ISBN: 978-989-758-504-3
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
it is related to all the human activities that emit in the
atmosphere. Looking at a micro-scale area, new fac-
tors can be added: for example, the city architecture
can influence the wind direction and speed resulting
in different dispersion scenario or the implementation
of a green area can decrease local contamination.
In order to restrain the continuous pollution and to
try restoring some area to a better status, worldwide
regulations have been issued: most of them imposed
concentration limits both for outdoor air regarding
pollutants like carbon monoxide, BTEX (Benzene
- Toluene - Ethylbenzene - Xylene), nitrogen ox-
ides, particular matter, and limit for specific emitting
sources as industrial plants and human activities with
the use of organic solvents. The overall strategy is to
limit the emitting source where and when it is possi-
ble, and to check the air quality as result of the previ-
ous described factor. With the collected data, logged
from stationary and mobile station, there is the pos-
sibility to assess the air quality by the use of some
indicators based on the detected concentration of spe-
cific pollutant. A similar indicator is used in Europe,
namely European Air Quality Index (EAQI), as estab-
lished by 2008/50/CE directive. The hourly index is
based on concentration values for up to five key pollu-
tants and it reflects the potential impact of air quality
on health, driven by the pollutant for which concen-
trations are poorest due to associated health impacts.
The data are collected by stationary stations managed
by local authorities.
In Italy, the transposition of the European di-
rective took place with the enactment of law D.lgs.
155/2010 that has established a unified regulatory
framework for the assessment and management of
ambient air quality. Regions are assigned the respon-
sibility to assess this quality, to classify the regional
territory into zones and agglomerations, and to draw
up plans and programs to maintain ambient air quality
where it is good and to improve it in other cases. The
national law imposes limits to outdoor pollutants con-
centration and, since the private transport sector has
been identified as the major contributor to city pollu-
tion, in case of exceeding daily limits, the city admin-
istration restricts the access for private transport.
In this scenario, the implementation of forecasting
modeling systems have become increasingly impor-
tant in order to understand the future impacts of the
human activities and to manage local areas. Forecast-
ing can both be applied to the new emitting sources
in order to understand their relative impact on local
air, and directly to outdoor air quality to understand
its development. It is clear that in the first case the
problem resolution is easier since all the factors that
affect the final result are well known (characteristics
of the emitting source, pollutant concentration, plant
layout, etc.). In the second case, the factors that took
part in the game are various and not always so well
defined: indeed, the local impact detected by station-
ary stations is due to a series of events such as par-
ticular wind direction, local traffic, presence of a new
apartment blocks and so on. Hence, in the situation of
a micro-scale forecasting, the boundary between the
influence classes for the air quality is very blurred. To
help solve this problem, the use of machine learning
techniques seems to be a promising practice.
In the last years, many scholars have studied the
implementation of forecasting modeling with ma-
chine learning: the results may significantly vary, de-
pending on the used dataset and the implementation
made. The machine learning help is based on the as-
sumption of a black box mechanism for the air qual-
ity: the forecast is essentially based on the training’
on a specific dataset, which results in the extrapola-
tion of a statistical set of rules that can be applied to
the newly collected data. Globally, the shown trends
indicate an improvement in the forecast on an ex-
tended area, such as a region, or at national level, with
high level datasets. The forecast buffer time can also
vary according to the used mechanism.
In this paper, an application of a forecasting mod-
eling approach implemented by a machine learning
based technique is presented for an Italian city where
air quality is assessed by means of stationary stations
controlled by local authorities according to D.lgs.
155/2010. The aim of this research is to understand
if this application can lead to a good forecast on a
focused area with a few analyzing stations and local
weather stations in order to better manage the area be-
fore the limits imposed are exceeded. The main origi-
nal contribution is the application of this kind of anal-
ysis on combined official pollution and weather data
about Campania region: at the best of our knowledge,
no such analysis is available in literature. In addition,
at the moment as per practice the data collected from
the station are firstly validated by a third part before
they are used for forecasting purposes. Indeed, this
quality check and assurance (QC/QA) is an essential
phase and it is usually handmade by few technicians.
For these reasons, it could easily be affected by errors
and hence data loss. Consequently, for this research
we only used raw data in order to check how they per-
form without any preliminary screening.
After this section, the paper is organized as fol-
lows: next section presents related work, and a brief
background on this field is summarized; then the
case study and the used dataset are described; sub-
sequently, the methodology used to develop the fore-
casting model by means of machine learning; results
Applying Machine Learning to Weather and Pollution Data Analysis for a Better Management of Local Areas: The Case of Napoli, Italy
and discussion close the paper, together with future
work and developments.
Several type of forecasting methods have been dis-
cussed in literature, regardless of a specific context as
in (Chatfield, 2000) or (Hyndman and Athanasopou-
los, 2018). Forecasting methods can be in general di-
vided into three main categories: those that only deal
with expert opinion, those based on physical models
(Brandt et al., 2001), and those that instead make pre-
dictions based on a statistical analysis of values in a
series (Armstrong, 2001).
A discussion based on published reviews and re-
cent analyses about challenges, applications and ad-
vances, main gaps and trends along with research
needs for atmospheric composition and air quality
modeling and forecasting can be found in (Baklanov
and Zhang, 2020).
A model to predict emission concentrations of
, SO
, O
for a selected number of forward time
steps is proposed in (Doma
nska and Wojtylak, 2014)
and named e-APFM. It requires large historical data
series for weather forecast, meteorological and pol-
lution data enriched with information about the wind
direction in sectors and meteorological stations.
Wind strength and direction is a key feature in
the propagation of pollutants. In (Contreras and
Ferri, 2016) several regression models have been
compared to be able to predict the levels of four dif-
ferent pollutants (CO, NO
, SO
, O
) in several lo-
cations of a city. Different techniques to incorporate
wind strength and direction in the regression models
have been studied and applied within an interpolation
A recent review on the intelligent modeling strate-
gies in the air quality forecasting has been published
(Liu et al., 2021) with the summarizing of representa-
tive and efficient predictive models along with some
possible research directions of the air pollution fore-
casting and monitoring (Campanile et al., 2020).
A feature ranking method to forecast multiple air
pollutants simultaneously over two cities is proposed
in (Masmoudi et al., 2020). It is based on a combina-
tion of an ensemble method for Multi-Target Regres-
sion problems and the RandomForest permutation im-
portance measure, so allowing to obtain good results
even with a restricted subset of features.
A spatiotemporal air pollution analysis that in-
volves large geographical areas and spans over a long
time period can be surely classified as a big data prob-
lem. In (Tong, 2020) the state-of-the-art machine
learning and deep learning methods are introduced for
the generation of more accurate estimations of PM
A framework for air pollution monitoring and
forecasting that combines deep learning and domain-
decomposition techniques to allow model deployment
extending beyond the domain(s) on which it has been
trained is presented in (Haehnel et al., 2020).
Neural networks have been applied in pollution
forecasting: AirPoolTool, an online tool using neu-
ral networks, publishes +1, +2, and +3 days predic-
tions of air pollutants updated twice a day (Kurt et al.,
2008); a deep multi-output LSTM (DM-LSTM) neu-
ral network model incorporated with three deep learn-
ing algorithms is presented in (Zhou et al., 2019);
a model using Artificial Neural Networks (ANN)
to forecast pollutant concentration in a highly pol-
luted region, trained using meteorological parame-
ters equipped with real time correction is presented
in (Agarwal et al., 2020); an approach for particulate
matter (PM
) prediction for Delhi with both Support
Vector Machines and ANN is described in (Masood
and Ahmad, 2020); a parameterised non-intrusive re-
duced order model (P-NIROM) based on proper or-
thogonal decomposition and machine learning meth-
ods has been developed to model reduction of pollu-
tant transport equations in order to build a rapid re-
sponse tool for regional air pollution modeling (Xiao
et al., 2019).
Pollution forecasting can be improved by us-
ing real-time data from sensors: a wireless sen-
sor network that gathers pollutant concentrations has
been used in Bengaluri city in South India (Belavadi
et al., 2020), and IoT-based techniques with vehicles
equipped with sensors embedded have been experi-
mented (Shetty et al., 2020) that dynamically help the
prediction of pollution level in the vehicle location
based on the previous and current data.
From all the reviewed works it is clear that, in
order to achieve a good pollution forecasting, it is
mandatory to combine emission values detected by
sensors with meteorological conditions, in particular
wind strength and direction. The origin and the qual-
ity of acquired data is also a key factor for the success.
Regione Campania, the authority governing the
homonym region located in southern Italy, according
to D.Lgs. 155/2010 has implemented an air monitor-
ing network by using mobile and stationary stations.
After the last upgrade, the network configuration in-
cludes 36 fixed monitoring stations and 5 mobile lab-
AI4EIoTs 2021 - Special Session on Artificial Intelligence for Emerging IoT Systems: Open Challenges and Novel Perspectives
oratories directly operated by the local environmental
protection agency (ARPAC) plus 6 more fixed stations
owned by third parties. The location of each station
was defined in order to have a capillary disposition
on the whole area and, at the same time, to cover
all the sensitive receptors: hence, the stationary sta-
tions are usually located on the roofs of schools, hos-
pitals and so on. Mobile laboratories are used accord-
ingly to the necessities. Besides this network, there is
another one, entirely devolved to analyse air quality
near waste treatment plants: in this work this second
network is not taken into consideration. The stations
analyse the pollutant prescribed by D.Lgs. 155/2010
based on their locations: generally, the pollutants are
nitrogen oxide, carbon monoxide, particular matter
(P.M. both 10 and 2.5), ozone, benzene and sulphur
dioxide. Data are collected hourly and then validated
by applying national guidelines: after the validation
process, the dataset is used for different purpouse by
ARPAC while the raw data are available to the pub-
lic. In addition, since 2018, some of the stations have
been equipped with meteorological station to collect
site-specific information. Collected data are basically
used by the European Commission to create a specific
air quality map (European Environmental Agency, ).
Actually, data are also used to implement a fore-
casting system that is the base of official reports dis-
closure. ARPAC uses mathematical models to pre-
dict the spatial and temporal distribution of pollutants
over a given area. In this context, the behaviour of the
atmosphere in its layers in contact with the ground
is decisive, where convective motions, thermal inver-
sions and turbulence develop as a result of solar ra-
diation and terrestrial re-irradiation. Meteorological
monitoring is managed by CEntro MEtereologico e
Climatologico (CEMEC) by means of the same sta-
tions, which implement specific sensors. The fore-
casting analysis is developed for a time window of
three days and it has a low resolution, meaning that
the resulting isopleths (curves showing the same pol-
lutants concentration) cover a large area (Figure 1).
For this work, the first check was made on the
open data regarding the concentration and the meteo-
rological specifics. The open data related to pollutants
concentration span since 2006 to 2018 (ARPAC, ),
while data about meteorological conditions are avail-
able since 2018 (Centro Meteorologico e Climato-
logico, ). In addition, not all the stations are able to
determinate the required pollutants: indeed, the sen-
sors need to be checked frequently, hence they can be
out of use for maintenance or, essentially, may be not
designed to cover all kinds of analyses.
The spatiotemporal discrepancy is analysed in or-
der to choose the boundary system. By matching the
Figure 1: Screenshot of the hourly forecasting of PM
made by ARPAC for February 26th, 2021. The area anal-
ysed was all the Campania region. The major cities are
identified with the black dots. In order to give a distance
set-up, it is highlighted that the distance Napoli - Caserta is
30 km circa. The prediction is calculated for three days.
two datasets, it is clear that the discrepancy is over-
come only since 2018 and only for those stations that
have recorded meteorological data. Figure 2 shows
the location of all the stations that provide both mete-
orological and concentration data and in Table 1 sta-
tions coordinates are reported.
We decided to work only on the stations in the city
of Napoli, so to restrict the area to be studies, aiming
at getting a better resolution for that specific city. The
chosen stations are reported in Table 2 with the spec-
ification of the pollutants analysed by each of them.
Figure 2: Satellite image of Napoli gulf (south-west), part
of Salerno gulf (south) and Piana Campana (north). The
red dots represent the locations of the stations with both
meteorological and concentration acquisition systems.
One of the major concerns in any experiment which
tries to extract information from raw data, to obtain
Applying Machine Learning to Weather and Pollution Data Analysis for a Better Management of Local Areas: The Case of Napoli, Italy
Table 1: Stations locations.
Station Code Name Long Lat
IT0898A NA06 14.25 40.85
IT0934A BN32 14.78 41.13
IT0936A AV41 14.78 40.91
IT1491A NA07 14.27 40.85
IT1493A NA09 14.35 40.85
IT1504A SA22 14.77 40.68
Table 2: Specific pollutants analysed by single station. T
stands for true, hence the contaminant is analysed. F stands
for false, so the contaminant is not analysed.
Name C
NA06 T T T F F
BN32 F F T F F
AV41 T F T T F
NA07 T T T F T
NA09 T T T F F
SA22 F F T F F
hidden information and make predictions by machine
learning algorithms or other techniques, is the data
collection phase.
Data used in this experiment have been collected
by ARPAC fixed stations located in the area managed
by Regione Campania; those data belong to two main
types of information: air pollution data and weather
data. Once acquired, those datasets should be merged
to have an unique dataset that includes all the values.
The resulting dataset is a good starting point for data
analysis and for applying regression models to obtain
Besides the P.M., which is not revealed by these
stations, another important parameter to consider and
worth to predict is the CO: like the other compounds,
the carbon monoxide is used to classify the regional
area but, in contrast with the others, its average bench-
marks are calculated every 8 hours, hence the preci-
sion of the forecasting should be superior than that of
the forecasting of a pollutant with an average bench-
mark equal to 1 year.
4.1 Dataset Description
The air pollution dataset is downloaded from the of-
ficial ARPAC website (ARPAC, ). The downloaded
dataset is in csv (comma separated values) format and
contains the entire acquired data volume. It is an ag-
gregate file that contains data from all sensors and all
stations collected hourly for year 2018.
The format used for the measurements in the csv
file is rather inconvenient for data analysis, as for each
sensor and station a different row is present. We have
transformed the dataset to obtain a file which has all
sensors value for each hour on the same line in order
to make the dataset more compact and manageable.
Parameters in the dataset are summarized in Ta-
ble 3: the relevant difference in the number of mea-
surements among the parameters depends on stations,
as stations only perform some measurements, like de-
scribed before.
Table 3: Parameters of air pollution dataset.
Parameter Description Unit n.
Benzene mg/m
CO Carbon mg/m
Nitrogen mg/m
Ozone mg/m
Sulfur dioxide mg/m
Weather data are downloaded from CEMEC web-
site. Differently from the previous case, in this one
a data file for each day must be downloaded. Each
file contains the measurements of a sensor and sta-
tion per line. Before applying the same transforma-
tion performed on the air pollution dataset, all files
have been merged in order to have an unique and con-
tinuous dataset.
Parameters in this dataset are summarized in Table
Table 4: Parameters of weather dataset.
Parameter Description Unit n.
AlbeInf Lower W /m
AlbeSup Upper W /m
Rainfall - mm 139,601
RadSG Day W /m
RadSN Night W/m
Temperature - C 155,250
Humidity - % 141,235
Pressure - hPa 134,735
UVA - W /m
UVB - W/m
Wind - degrees 70,149
Wind speed - m/s 70,232
AI4EIoTs 2021 - Special Session on Artificial Intelligence for Emerging IoT Systems: Open Challenges and Novel Perspectives
4.2 Data Fusion
Once we had the raw data of both datasets available
in a manageable form, to maximize possible exploita-
tion the weather and the air pollution databases were
merged on same common information: the date and
time value of the measurement and the identifier of
the station which measured them. This part is crucial
to preserve data correctness and to expand the useful
information content of the datasets.
Performing this transformation required coping
with the imbalance in the number of values avail-
able for the parameters in both datasets. To address
this issue we decided to utilize a maximum inclu-
sion strategy by performing the joining action with
”outer” mode to preserve the largest quantity of data
and avoid data loss.
If, on one hand, this procedure has allowed to ben-
efit of all available data, on the other hand it has added
a new issue: the resulting dataset has a large num-
ber of missing elements caused by the missing values
for same parameters in the source datasets and am-
plified by the magnitude effect of Cartesian product
performed in the merging process.
4.3 Data Analysis
The data analysis of the new comprehensive dataset
has been executed mainly in Jupyter Notebook envi-
ronment using the Python programming language and
the Pandas library.
The first step was oriented to obtain an insight into
the properties of each attribute of the dataset; the table
in figure 6 summarizes descriptive statistics of data.
As regression analysis could suffer bad perfor-
mances if there are highly correlated input features,
it is crucial to investigate the correlation between in-
put and output attributes. Consequently, in order to
guide a proper features selection, the second step ex-
plores the relationship between couples of variables:
the most common method for calculating this is Pear-
son’s Correlation Coefficient. Figure 7 shows the cor-
relation matrix in which it can been seen the correla-
tion between all pairs of attributes. Figure 3 shows
the correlation matrix in graphical form.
Finally, a graphical analysis has been performed to
point out the characteristics of the attributes. Figure
4 and in Figure 5 provide a general outlook of the
distribution of each attributes.
The data analysis reveals some interesting facts
about the attributes. The AlbeInf, AlbeSup, RadSG,
RadSN, UVA and UVB have averagely high correla-
tion among them. It was widely expected, since they
measure different aspect of the solar radiation.
Figure 3: Correlation matrix plot.
In addition, the CO parameter has a moderate cor-
relation with C
, NO
and SO
, differently from all
the other attributes with which there is no evidence of
considerable correlation.
The last step in this phase consists in feature selec-
tion and data cleaning, therefore we drop some fea-
tures from the original dataset and drop all the lines
with missing data.
As final result of data transformation, cleaning
and features selection, we have obtained a dataset
with the following features: C
, NO
, SO
, Rain-
fall, Temperature, Humidity, Pressure, Wind direc-
tion, Wind speed, and finally CO as target variable.
4.4 Evaluation Metrics
To validate the ability of the regression model to make
good predictions, the dataset has been divided into a
training and a test part, 70% and 30% respectively.
The Mean Square Error (MSE), the Mean Absolute
Error (MAE), and the Coefficient of determination
) have been calculated to evaluate the performance
of prediction.
MAE(y, ˆy) =
MSE(y, ˆy) =
(y, ˆy) = 1
where ¯y =
MAE and MSE are risk metrics corresponding to
the expected value of the error and the quadratic error,
while R
( 1) represents the proportion of variance
of y and provides a general indication of goodness of
fit of the model.
Applying Machine Learning to Weather and Pollution Data Analysis for a Better Management of Local Areas: The Case of Napoli, Italy
Figure 4: Outlook of distribution attributes.
4.5 Regression Models
In order to perform the analysis, we applied four dif-
ferent regression models, namely the Linear Regres-
sion Model calculated with Ordinary Least Square
(LN-OLS), the Ridge regression model (Ridge), the
Lasso model (Lasso) and Supervised Nearest Neigh-
bors Regression (KNN).
We use the notation x R
to describe the input
data, with m input features, y for the target variable
(CO). The Linear Model tries to approximate the pre-
dicted value ˆy using a linear combination of the input
ˆy(w,x) = w
+ w
+ ... + w
We also use the notation X to describe the matrix
of input features and w = (w
) for the vector
of coefficients. Mathematically, the solution of the
following problem provides us with the values of the
coefficients w of the linear model, using the afore-
mentioned methods:
OLS : min
kXw yk
Ridge : min
kXw yk
+ αkwk
Lasso : min
kXw yk
+ αkwk
The KNN was selected because it is a non linear
algorithm which uses a different approach, on a differ-
ent basis with respect to the other three chosen ones:
consequently, it is not possible to define an analogous,
yet consistent, formal expression.
To implement the various regression algorithms, we
have used Python and the scikit-learn programming
AI4EIoTs 2021 - Special Session on Artificial Intelligence for Emerging IoT Systems: Open Challenges and Novel Perspectives
Figure 5: Outlook of attributes density.
Figure 6: Statistical outlook of attributes of the dataset.
library. We use a cross-validation approach, in or-
der to estimate the overall performance of the chosen
machine learning algorithms with less variance than
in the case of a single train-test split. The procedure
starts by dividing the dataset in k parts, with k = 10 in
our tests; then the algorithm is trained on k 1 parts.
The result is a more reliable estimation of the perfor-
mance of the machine learning algorithm.
We have performed tests using variables X =
, NO
, SO
, Rainfall, Temperature, Humidity,
Pressure, Wind direction, Wind speed) as features and
variable y = CO as target. We have repeated tests for
each of the above considered machine learning algo-
rithms, obtaining comparable measurements of per-
formance. The results are summarised in Table 5.
Table 5: Results.
Model R2 MAE MSE
LR-OLS 0.73 0.12 0.04
Ridge 0.77 0.12 0.04
Lasso 0.60 0.13 0.05
KNN 0.68 0.12 0.04
This analysis intentionally uses only regression
models: the prediction of CO from weather measures
and other pollution-related values was not a straight-
Applying Machine Learning to Weather and Pollution Data Analysis for a Better Management of Local Areas: The Case of Napoli, Italy
Figure 7: Pearson correlation between attributes of dataset.
forward task for the regression machine learning al-
gorithm, but with a R
value of 0.77 for the Ridge
algorithm and a low value for both MAE and MSE, it
becomes suitable for prediction.
In this paper we studied the applicability of a simple
machine learning technique on real open data man-
aged by third parties. Specifically, the analysed case
study is the Campania region (southern Italy), where
ARPAC, the local environmental protection agency,
developed and managed an air monitoring network by
using mobile and stationary stations. The same net-
work overlays with another network regarding mete-
orological measurements.
The first step was the analysis of available data
and the creation of a merged dataset so to overcome
the spatiotemporal discrepancy. Indeed, even if the
same station can perform both the chemical analy-
sis and meteorological recording, the two networks
work on different layers; hence, data are recorded in
two different datasets with different characteristics.
The challenge was overcome with the use of identi-
fiers that linked the same station between the dataset
used. It is important to highlight that, other from
”outer” mode used to preserve the largest quantity of
data and avoid data loss, the data were not screened
for their coherence. This decision was made in or-
der to understand how coarse data perform with the
machine learning technique. According to the cor-
relation analysis performed on CO trends, this pa-
rameter emerged to have a moderate correlation with
, NO
and SO
, differently from all the other at-
tributes with which there is no evidence of consider-
able correlation. This correction is explained by the
characteristics of the compounds: all of them are re-
lated to vehicular traffic emissions. Next, four differ-
ent regression models, namely the Linear Regression
Model calculated with Ordinary Least Square (LN-
OLS), the Ridge regression model (Ridge), the Lasso
model (Lasso) and Supervised Nearest Neighbors Re-
gression (KNN) were applied and evaluated by statis-
tical means. Finally, the Ridge regression model was
found to be the choice that fits the best among them
with an R
value equal to 0.77 and low value for both
MAE and MSE, equal to 0.12 and 0.04 respectively.
In this work, using regression algorithms we only
scratched the surface in data prediction. The search
for a better prediction should consider the use of deep
learning algorithms: as a matter of fact, this method-
ology can analyse large datasets with considerable
performances. Nevertheless, it is clear that perform-
ing an headline validation of the starting data can help
both the techniques, as, at the moment, a human in-
tervention in the first phases is still needed to address
the right directions in data exploration.
This work has been partially funded by the internal
competitive funding program “VALERE: VAnviteLli
pEr la RicErca” of Universit
a degli Studi della Cam-
pania “Luigi Vanvitelli” and by project ”Attrazione
e Mobilit
a dei Ricercatori” Italian PON Programme
(PON AIM 2018 num. AIM1878214-2).
AI4EIoTs 2021 - Special Session on Artificial Intelligence for Emerging IoT Systems: Open Challenges and Novel Perspectives
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Applying Machine Learning to Weather and Pollution Data Analysis for a Better Management of Local Areas: The Case of Napoli, Italy