Using Machine Learning to Forecast Air and Water Quality
Carolina Silva
, Bruno Fernandes
, Pedro Oliveira
and Paulo Novais
Department of Informatics, ALGORITMI Centre, University of Minho, Braga, Portugal
Environmental Sustainability, Machine Learning, Tree-based Models, Deep Learning.
Environmental sustainability is one of the biggest concerns nowadays. With increasingly latent negative im-
pacts, it is substantiated that future generations may be compromised. The research here presented addresses
this topic, focusing on air quality and atmospheric pollution, in particular the Ultraviolet index and Carbon
Monoxide air concentration, as well as water issues regarding Wastewater Treatment Plants, in particular the
pH of water. A set of Machine Learning regressors and classifiers are conceived, tuned, and evaluated in regard
to their ability to forecast several parameters of interest. The experimented models include Decision Trees,
Random Forests, Multilayer Perceptrons, and Long Short-Term Memory networks. The obtained results as-
sert the strong ability of LSTMs to forecast air pollutants, with all models presenting similar results when the
subject was the pH of water.
Over the last few years, more data have been gener-
ated than ever. Consequently, many organizations,
whether business or public entities, identifies data
as a valuable asset that supports its decision-making
process ((Hino et al., 2018; Sandryhaila and Moura,
2014)). On the other hand, Machine Learning (ML), a
field from Artificial Intelligence (AI), has been grow-
ing in importance in the last decade ((Qiu et al.,
2016)). This field concerns data collection, analysis,
and then the prediction of several meaningful param-
eters. ML is used in many sectors nowadays, once it
is recognized as a field that can provide accurate and
real-time solutions. The various entities that operate
in the most diverse areas can, with ML, acquire the
ability to act before something occur, avoiding and
preventing negative impacts ((Qiu et al., 2016)).
Since ML can be implemented in a vast range
of areas, it is interesting to apply it in a way that
can make a difference, aiming for the common good.
From this emerges an area with important societal im-
pact: environmental sustainability. Sustainable de-
velopment, from an environmental point of view, has
been a significant problem for countries for decades
since negative environmental impacts are increasingly
perceived and verified ((Isabel Molina-G
omez et al.,
2020)). Due to the registered population growth,
the demand for natural resources has, consequently,
raised. Further, industrial activities are increasingly
pronounced in developed countries, being the key to
their economic sustainability. All of this generates
anthropogenic emissions, which damage the environ-
ment, causing visible impacts such as public health
problems, climatic changes, and many others. All this
can compromise future generations. To avoid these
risks, responsible entities need to make decisions to
reduce its negative impacts and consequent hazard for
the population ((Sarkodie, 2021)).
Aiming to address important points on the scope
of environmental sustainability, this research focuses
on the conception, tuning, and evaluation of multiple
ML models to anticipate possible problematic situa-
tions. Thus, it allows one to act in a preventive way
to provide the best for the population and future gen-
erations. Further, it allows one to better allocate re-
sources and consequently, maximize potential bene-
fits for companies as well as the human being.
Throughout this research, the CRISP-DM
methodology was the one being followed to ensure
a proper progress of this study. Overall, the main
goals of this research are to: (1) Forecast parameters
in the air quality domain, including the Ultraviolet
(UV) index and the Carbon Monoxide (CO) air
concentration; (2) Forecast parameters in the water
quality domain, in particular the pH of water, con-
Silva, C., Fernandes, B., Oliveira, P. and Novais, P.
Using Machine Learning to Forecast Air and Water Quality.
DOI: 10.5220/0010379312101217
In Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021) - Volume 2, pages 1210-1217
ISBN: 978-989-758-484-8
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
cerning Wastewater Treatment Plants (WWTPs),
in the exact moment before it returns to natural
sources; (3) Perceive which are the relevant features
concerning the forecasting process, evaluated by the
quality of the outcomes generated by each model;
(4) Understand, compare, and tune tree-based models
(Decision Trees (DTs) and Random Forests (RFs)),
Multilayer Perceptrons (MLPs), and Long Short
Term-Memory networks (LSTMs).
The remaining of this paper is structured as fol-
lows: the next section describes the current state of
the art regarding the UV index and CO impacts, as
well as the WWTP context, exploring its operations
and the measured parameters. Material and meth-
ods are detailed in the following section, showing the
data exploration process, its preparation, and the used
technologies. The fourth section focuses on the con-
ducted experiments, showing the conceived scenarios
and the hyperparameters’ searching space. The fifth
section discusses the obtained results. Finally, con-
clusions are drawn and future work is outlined.
Environmentally sustainability is a problem that cov-
ers the effort to establish green management, taking
into account current and future generations. Atmo-
spheric pollution, more specifically air pollution, is
an important environmental issue, primarily linked to
urban conditions and industrial emissions. This prob-
lem has grown, resulting from an increase in polluting
industries, making human activity its central source
((Cohen et al., 2017)). One of the most well recog-
nized primary pollutants is CO. This pollutant con-
stitutes a problem, causing some negative health im-
pacts. The biggest issue of CO exposure is tissue hy-
poxia (oxygen deficiency in tissues) due to its capa-
bility to bind the hemoglobin, blocking the tissues to
enough oxygen ((Lee et al., 2020)). Further, there
are other problems appointed for CO exposure such
as neurological sequels in humans, particularly neuro-
cognitive impairment and behavioral abnormalities in
children ((Block et al., 2012)).
UV radiation is yet another topic on air quality
that concerns the human health. Over the years, the
ozone layer has become thinner, leading to danger-
ous UV radiation reaching Earth’s surface. To in-
form the population of this radiation risk and cul-
tivate changes in mindset and attitudes against ex-
posure to the sun, the UV index was created ((Igoe
et al., 2013)). On a controlled scale, the UV radia-
tion has numerous benefits: it suppresses stress, im-
proves sleep, prevents some illness, and increases the
production of vitamin D in the human body ((Nor-
val et al., 2011)). However, it may cause severe
health problems such as ocular melanoma, skin can-
cer (melanoma and non-melanoma skin cancers), and
premature skin aging when exposed to high UV levels
((Norval et al., 2011)).
Water is an indispensable resource for the human
being. It is used in many contexts: at home, indus-
tries, for agriculture, among many others. So, it is
crucial to understand what happens to the residual
water that results from that use. In fact, first, wa-
ter is collected and transported to a plant in water
pipes (a network that connects the water sources to
the plant). Here, the residual water, commonly called
wastewater, is treated and returned to water sources in
environmentally safe conditions. This plant is called
A WWTP has a crucial role in Environmental Sus-
tainability. There are four fundamental phases in
WWTPs operations. The first one is known as the pre-
treatment, being here where coarse solids and floating
materials are removed from the wastewater. The sec-
ond is the primary treatment. At this phase, the re-
maining solids are removed as well as organic matter,
mainly by the action of gravity, using a primary clar-
ifier. Then, it occurs the secondary treatment, where
the biodegradable organic matter is removed, along
with suspended solids and nutrients. This phase can
be divided in two stages: (1) the aeration tank (the
anoxic and aerated zone) and (2) the secondary clar-
ifier. The last crucial phase is the tertiary treatment,
in which the remaining solids are removed, together
with organic matter and toxic compounds. After that,
the water is ready to be discharged to the surface. Ad-
ditionally, there is a sludge line in which the solids
that left from the previous steps are treated ((Spell-
man, 2013)). Figure 1 shows a simplified WWTP op-
eration process.
Within a WWTP, there are several parameters that
need to be controlled and evaluated in the different
stages of the operation process. Among these param-
eters one may find the temperature, conductivity, al-
kalinity, pH, Nitrogen, dissolved oxygen, solids, bac-
teria, among others. However, one of the most signif-
icant wastewater characteristics is its pH. When less
than 5, it indicates acid water, while values higher
than 9 indicate it is alkaline. Identify the pH value in
a WWTP is essential since this is a controlling agent
of the biological and physical-chemical wastewater
functions. This parameter deserves special attention,
mainly in the aeration tank. Here occurs a biological
treatment, with the action of several microorganisms
that need the right pH conditions to do its job ((Spell-
man, 2013)). At the exit of a WWTP (the discharge
Using Machine Learning to Forecast Air and Water Quality
Figure 1: Simplified scheme of a WWTP operation.
process), the pH value must comply with the emis-
sion limit values considered by the law. According to
the Portuguese Environment Agency ((PAE, 2013)),
these values must be between 5.5 and 9 to be consid-
ered “good”, and between 6.5 and 8.5 to be classified
as “excellent”.
Over the years, several studies were carried out
addressing the related topics, air, and water quality.
Literature shows some researches addressing the UV
index forecast. It stands out the using of a CART
regression model in the presence of several environ-
mental conditions. This study shows that when ob-
served opacity are generated better predictions com-
pared to using clear-sky UV ((Burrows, 1997)). Fur-
ther, addressing the atmospheric pollutants, a study
in the city of Hangzhou, in China, was deployed.
This research makes use of Recurrent Neural Net-
work (RNN) and RFs for analysis and accurate fore-
cast of several atmospheric pollutants (including CO),
based on the prediction of the future 24h ((Feng et al.,
2019)). Furthermore, regarding this topic, recent re-
search exposes the use of ML to forecast air quality
in the city of California. Using Support Vector Re-
gression (SVR) was hourly predicted pollutant con-
centrations, like CO, sulfur dioxide, nitrogen dioxide,
ground-level ozone, and particulate matter ((Castelli
et al., 2020)).
Regarding WWTPs, some studies have already
engaged on using a hybrid statistical-ML approach
to forecasting the ammonia presented in the activated
sludge, to improve the control process of WWTPs
((Gawdzik et al., 2016)). Further, a few studies have
already focused on the use of ML models to moni-
tored the WWTPs operations ((Harrou et al., 2018;
Dairi et al., 2019)).
The next lines describe the materials and methods
used in this work, including the used datasets, to
achieve the proposed goals.
3.1 Data Exploration
Two datasets support this research. The first con-
cerns the air quality of a Portuguese city, while the
other presents water quality features with respect to a
WWTP within the same city.
The first dataset reveals data about the UV index
(UV Value), air pollutants concentration (CO Value
and SO
Value), and weather conditions (Clouds,
Temperature, and Weather Description). It presents
11817 hourly observations in a time range that goes
from 24-07-2018 to 23-03-2020, with some excep-
tions being observed. There are no missing values.
An analysis to the UV Value and the CO Value shows
that, as expected, these data do not present a signifi-
cant variation through a day.
Figure 2 illustrates the mean monthly UV index
behavior in the time range under study. It shows that
this feature presents a well-defined behavior, reveal-
ing that during July it reached the highest value, and
the lowest in December, on average.
Figure 3, on the other hand, shows the CO con-
centration over time, in the defined time interval. The
graph illustrates that higher values are reached in Oc-
tober as well as the presence of missing time steps.
The second dataset contains water quality data
from a real WWTP. The main features are the Dis-
solved Oxygen (DO) (from the anoxic and the aerated
zones), the water pH from the secondary clarifier, and
ICAART 2021 - 13th International Conference on Agents and Artificial Intelligence
Figure 2: Average UV index per
Figure 3: CO concentration over time. Figure 4: pH in the tertiary treatment
unit over time.
the temperature from the tertiary treatment unit. This
dataset presents a total of 2379 observations defined
in a time range that goes from 2016-01-01 to 2020-
05-28. These dataset presents missing values.
Figure 4 illustrates the pH, in the tertiary treatment
unit, over time. It depicts that the values mostly com-
ply with those defined by the Portuguese Environment
Agency (illustrated by the horizontal lines).
Finally, Table 1 depicts the available features in
both datasets.
Table 1: Features available in both datasets.
Dataset Feature (unit)
UV Value
CO Value (ppm)
Value (ppm)
Clouds (%)
Temperature (ºC)
Weather Description
Date (YYYY-MM-DD HH:mm:ss)
DO-Aerated Zone (mg/L)
DO-Anoxic Zone (mg/L)
pH-Secondary Clarifier
Temperature-Tertiary Treatment
Date (YYYY-MM-DD HH:mm:ss)
3.2 Data Preparation
With respect to the air quality dataset, the UV Value
and the CO Value (target features) do not present a
significant variation during a day. For this reason,
data were grouped by day, using the mean value of
the UV Value, CO Value, SO
Value, Clouds, and the
Temperature. Further, for the nominal feature, the
Weather Description, the most frequent description in
a day was the one used, i.e., the mode. It resulted in a
dataset made by 506 daily observations. Furthermore,
since neural networks only accept numerical features
as inputs, the Weather Description was labeled en-
coded. Date fields were also extracted, creating three
new features: day, month, and year in regard to each
With respect to the water quality dataset, the main
transformations focused on handling the missing val-
ues. Most of these were computed through linear
interpolation, and when that was not possible, the
records were removed. After this process, the dataset
was left with 2149 observations. Date fields were
also extracted, creating four new features: hour, day,
month, and year in regard to each observation.
This research was originally a regression problem
due to the nature of the target features. It was settled,
however, that it might be of interest to explore the pre-
diction of the UV Value as a classification problem as
well. This feature was converted into different levels,
those established by the World Health Organization
(WHO), through the creation of four bins: Low (when
the UV index was between 0 and 2), Medium (UV in-
dex between 3 and 5), High (UV index between 6 and
7), and Very High (UV index higher than 8). No in-
dexes higher than 11 were found in the dataset.
3.3 Technologies
The technologies used to develop this research were
the KNIME software as well as Python, supported
by Keras and TensorFlow as main APIs. Besides,
other important libraries such as pandas, matplotlib,
NumPy, and scikit-learn were also used. To tune and
train the deep learning models the Google Colabo-
ratory cloud service was used. This service uses a
Jupyter notebook environment and runs totally in the
cloud, using GPUs.
After preparing the data, multiple scenarios were cre-
ated to study the impact of each feature in the models’
Using Machine Learning to Forecast Air and Water Quality
4.1 UV Index Scenarios
First, for the UV index forecasting, five scenarios
were built, representing a gradual increase in the fea-
tures that constituted them. Thereby, the scenarios
were as follows:
1. The first scenario has in its constitution the Date,
the CO Value, and the SO
Value, in order to un-
derstand the impact of the atmospheric pollutants;
2. Scenario number two adds the Temperature to the
previous one. Thus, it enables us to understand the
impact of this feature in the outcomes regarding
the UV index prediction;
3. The third scenario adds the Clouds percentage to
the preceding, allowing us to know the influence
of clouds percentage in the prediction of the UV
4. Scenario number four adds the month to the pre-
vious one, as the data shows that the UV in-
dex has a well-defined behavior according to the
month of the year (higher in the summer months
and smaller in the winter, in the Northern Hemi-
5. Scenario number five adds the Weather descrip-
tion to the prior, to perceive its impact on the mod-
els’ outcomes.
The scenarios described above are used to tune and
evaluate all tree-based models. To train the MLP
model, the same scenarios were used except for the
Date feature, which was replaced by the day, month,
and year. Therefore, scenario number four becomes
useless in this context.
The UV index was also framed as a time series
problem. In this context, LSTM models were con-
ceived and tuned using only the UV Value feature
(framed as a uni-variate problem).
4.2 CO Concentration Scenario
The CO air concentration forecast was exclusively
formulated as a time series problem, with a LSTM
model being conceived and tuned for a single feature,
the CO Value. This is the only scenario created for the
CO air concentration.
4.3 Water pH Scenarios
For water pH forecasting, four scenarios were created,
as follows:
1. Scenario number one is constituted by the Date,
the pH-Secondary clarifier, the DO-Aerated Zone,
the DO-Anoxic Zone, and the Temperature-
Tertiary Treatment. This scenario uses all the fea-
tures available from the water quality dataset;
2. Scenario number two excludes the temperature
feature from the previous one, allowing us to un-
derstand the impact of this feature in the water pH
3. The third scenario is constituted by the Date, the
DO-Aerated Zone, the DO-Anoxic Zone and the
Temperature-Tertiary Treatment. When compared
with the preceding, this one replaces the pH from
the secondary clarifier by the temperate, previ-
ously withdrawn;
4. Scenario number four is constituted by the Date,
the pH-Secondary clarifier, and the Temperature-
Tertiary Treatment. It excludes both dissolved
oxygen features to understand its impact on the
forecasts’ quality.
The above scenarios are used to train all tree-based
models. For the MLPs, the date feature is replaced by
four features, i.e., hour, day, month, and year.
4.4 Hyperparameters Searching Space
After building the feature scenarios, the models’ hy-
perparameters were set. Table 2 shows the search-
ing space considered for each hyperparameter of each
The best outcomes for each scenario, considering the
optimal hyperparameter combination, are presented
in the next lines. The used evaluation metrics are the
Accuracy and the Mean Absolute Error (MAE).
5.1 UV Index Forecasting
When considering the UV index as a classification
problem, the levels defined by WHO were the ones
used to set the target feature as Low, Medium, High,
and Very High. Table 3 presents the results generated
from this process for each model type.
The best candidate model achieved an accuracy
value of 93.5%. It is generated by the MLP model,
making use of the scenario constituted by the atmo-
spheric pollutants and the date fields as features (Sce-
nario 1). It may indicate that, for the MLP classifica-
tion problem, using the fewest features generate bet-
ter results in this context. Further, for the tree-based
models, Scenario 4 shows the best outcomes, which
ICAART 2021 - 13th International Conference on Agents and Artificial Intelligence
Table 2: Hyperparameters tested for each model.
Model Hyperparameter Search Space
Decision Tree
Quality measure [Gini Index, Gain ratio]
Pruning method [No pruning, MDL]
Reduced error pruning [True, False]
Minimum records per node From 1 to 15*
Missing value handling [XGBoost, Surrogate]
Limit number of levels From 1 to 20*
Min. node size From 1 to 15*
Random Forest
Split criterion [Information Gain (Ratio), Gini index]
Tree depth From 1 to 25*
Minimum child node size From 1 to 15*
Number of Models From 100 to 1200**
Tree depth From 1 to 15*
Minimum child node size From 1 to 15*
Number of Models From 100 to 1200**
Activation Function [relu, tanh, sigmoid]
Hidden Layers [1, 2, 3]
Learn Rate [0.001, 0.005, 0.01]
Neurons [16, 32, 64, 128]
Batch Size [16, 23, 30]
Time steps*** [7, 14, 21]
* With a step size of 1
** With a step size of 100
*** LSTM hyperparameter
Table 3: Accuracy values, per scenario, for the classification
of the UV index.
Scenario DT RF MLP
1 87.8% 79.6% 93.5%
2 77.9% 83.8% 91.1%
3 74.9% 82.2% 90.5%
4 92.1% 91.5% -
5 89.5% 91.3% 89.3%
proves that using the month name as a feature results
in the best accuracy.
DTs show their best performance (Scenario 4) us-
ing the Gini Index as the quality measure, no pruning,
and a minimum child node size of 2. Regarding this
model, were perceived that for all the scenarios, no
pruning revealed to be the best choice. It produces
better results, probably due to the low complexity of
the generated trees. So, all the branches are consid-
ered significant, and its removal leads to a decrease
in the model’s performance. For the RFs, the best
outcomes (again, Scenario 4) are generated using In-
formation Gain Ratio as quality measure, a tree depth
of 14, a minimum child node of 9, and a number of
models equal to 500, i.e., the number of trees to be
learned. Since the maximum number that was experi-
mented was 1200, the best outcome generated by this
model is produced with a medium level of complex-
ity. For the MLPs, the best candidate used 3 hidden
layers, a learning rate of 0.001, 64 neurons per layer,
a batch size of 23, and ’relu’ as activation function.
When considering the UV index as a regression
problem, the results are the ones presented in Table 4.
For the tree-based models, the best scenario is, again,
Scenario 4, which adds the month name to its con-
stitution. Further, for the MLP model, the best result
arises from Scenario 5. It shows that, regarding this
particular model, a higher amount of features gener-
ates the best outcome. Overall, the best model is a DT
with a MAE of just 0.37.
Table 4: MAE values, per scenario, regarding UV Index
Scenario DT RF MLP
1 0.589 1.438 0.490
2 0.789 0.739 0.514
3 0.901 1.128 0.521
4 0.373 0.410 -
5 0.433 1.598 0.452
For the DTs, the best performance (Scenario 4)
uses XGBoost to handle missing values, a tree depth
of 6, and a minimum node size of 5. For the RFs, the
best performance (Scenario 4) shows a tree depth of
11, a minimum child node size of 1, and a number
of models equal to 1000, a high value that shows the
need for a more complex model. For the MLP, the
best candidate used relu’ as the activation function, 2
hidden layers, a learning rate of 0.001, 32 neurons per
layer, and a batch size of 16 (Scenario 5).
Finally, the last approach concerns the UV index
Using Machine Learning to Forecast Air and Water Quality
as a time series problem. For this, LSTM models were
conceived, making recursive multi-step forecasts, i.e.,
the goal was to forecast the next three days. The
best LSTM candidate achieved an overall MAE of just
0.151, clearly outperforming the results obtained pre-
viously, with MLPs and the tree-based models. The
hyperparameter combination producing the best can-
didate makes use of the ’tanh’ as activation function,
a single hidden layer, a learning rate of 0.01, 16 neu-
rons per layer, and a batch size of 16 sequences. Fur-
ther, it uses 21 time steps to create a single sequence,
i.e., it uses the last three weeks to forecast the next
three days. Even though the best candidate does not
have a complex architecture, it still took a consider-
able amount of time to train, especially when com-
pared to the tree-based models.
5.2 CO Concentration Forecasting
The CO is one of the primary pollutants. Hence, in
this research, the air concentration of this pollutant
was set only as a time series problem. The same logic
that was used to forecast the UV index is applied to
the CO concentration, with the best LSTM candidate
reaching a MAE of 1.345×10
. The best hyperpa-
rameter combination uses a single layer, ’relu’ as ac-
tivation function, a learning rate of 0.005, 16 neurons,
and a batch size of 16. Moreover, using a sequence
of three weeks (in which the predictions are based)
shows to be the best option, i.e., time steps equal to
21. It indicates a simple model, taking into account
the maximum tested values. However, it also takes a
long time to run, as aforementioned.
5.3 Water pH Forecasting
The water pH is part of a WWTP, in particular, to its
tertiary phase, i.e., the moment right before the wa-
ter is discharged back to natural sources. This fore-
cast arises in order to try to mitigate the environmen-
tal consequences of these systems concerning water
Table 5: MAE values, per scenario, for water pH forecast-
Scenario DT RF MLP
1 0.106 0.114 0.119
2 0.184 0.180 0.146
3 0.195 0.193 0.154
4 0.106 0.120 0.118
By evaluating Table 5, one may conclude that all
the best candidate models produce very similar re-
sults, even though from different scenarios. The best
DT uses, as features, the pH from the secondary clari-
fier, the temperature, and the date (Scenario 4). How-
ever, it shows very similar outcomes when compared
with Scenario 1, which adds, as new features, the dis-
solved oxygen from the aerated and the anoxic zones.
Further, these two scenarios are also the ones depict-
ing the best solutions for the best RF and the best
MLP candidates. So, it is possible to conclude that,
when used together, the temperature and the pH from
the secondary clarifier are important features to fore-
cast the pH of the water from the tertiary treatment
unit. The dissolved oxygen features, by themselves,
do not seem to be a decent approach to predict water
The best candidate DT presents its best perfor-
mance (Scenario 4) using the surrogate as missing
value handling, a tree depth of 5, and a minimum
node size of 5. The best RF shows its best outcome
(Scenario 1) using a tree depth of 4, a minimum child
node size of 8, and 500 models, exhibiting a medium
complexity. On the other hand, the best MLP model
(Scenario 4) is constituted by 64 neurons per layer,
three hidden layers, a learning rate of 0.005, and a
batch size of 16. Again, this model took longer to
train when compared to the tree-based models.
Environmental sustainability is a big concern for en-
tities nowadays. The detrimental effects on public
health, ecosystem imbalance, and climate changes are
increasingly evident. Thus, this research conceived
and tune several supervised ML models, with dif-
ferent degrees of complexity, to multiple several pa-
rameters in the environmental sustainability domain.
Regarding the air quality and atmospheric pollution,
both the UV index and the CO air concentration were
addressed. For the UV index forecast, when treating
it as a classification problem, an accuracy of approxi-
mately 93% was achieved. In addition, predicting it as
a regression problem resulted in a MAE of 0.37 units.
For both approaches, the used features proved to be
relevant in the quality of the forecasts. Moreover, the
UV index was set as a time series problem using a
LSTM model. The best candidate model achieved
a MAE of 0.15, using a uni-variate approach. With
regard to the CO air concentration, a MAE value
of 1.345×10
was achieved, being possible to con-
clude that it is likely to predict these parameters with
small errors, using simple or more complex models.
The water pH was also predicted, achieving a
MAE of, approximately, 0.11 units, with the DTs,
ICAART 2021 - 13th International Conference on Agents and Artificial Intelligence
RFs and MLPs revealing themselves capable of pro-
ducing accurate forecasts. Furthermore, the used fea-
tures revealed themselves to have a significant impact
on the outcomes.
This research shows that it is possible to antici-
pate problematic situations and reduce their negative
impacts, with satisfactory accuracy. As future work,
the goal is to forecast the CO air concentration using
additional models, as well as distinct attributes. Ad-
ditionally, a major goal focuses on forecasting more
parameters related to Environmental Sustainability, in
order to promote a more sustainable and green soci-
This work was supported by National Funds through
the Portuguese funding agency, FCT - Founda-
tion for Science and Technology, within the project
DSAIPA/AI/0099/2019. The work of Bruno Fernan-
des is also supported by a Portuguese doctoral grant,
SFRH/BD/130125/2017, issued by FCT in Portugal.
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