=Paper=
{{Paper
|id=Vol-2853/short27
|storemode=property
|title=Air Pollution Prediction as a Source for Decision Making Framework in Medical Diagnosis
|pdfUrl=https://ceur-ws.org/Vol-2853/short27.pdf
|volume=Vol-2853
|authors=Valerii Lovkin,Andrii Oliinyk,Yurii Lukashenko
|dblpUrl=https://dblp.org/rec/conf/intelitsis/LovkinOL21
}}
==Air Pollution Prediction as a Source for Decision Making Framework in Medical Diagnosis==
https://ceur-ws.org/Vol-2853/short27.pdf
Air Pollution Prediction as a Source for Decision Making
Framework in Medical Diagnosis
Valerii Lovkina, Andrii Oliinyka and Yurii Lukashenkoa
a
National University “Zaporizhzhia Polytechnic”, Zhukovsky str., 64, Zaporizhzhia, 69063, Ukraine
Abstract
The problem of air pollution prediction is presented in the paper. It is considered regarding
complex problem of creation of decision making framework in medical diagnosis. Therefore
prediction is performed for a day, not for on an hour. The method of air pollution prediction
is developed using Long Short Term Memory (LSTM) recurrent neural network. The LSTM-
based model is used for prediction of concentration of separate air pollutant during the next
day based on its concentration during the previous hours and average traffic data. The
experimental investigation of the proposed method is performed by comparing it with
ARIMA model, multilayer perceptron, vanilla recurrent neural networks and LSTM. The
proposed method should be used in practice inside medical diagnosis tools and separate
systems for air pollution analysis, enabling to obtain predicted air pollutant concentration
level during the next day.
Keywords 1
Air pollution, road traffic, medical diagnosis, decision making framework, prediction,
machine learning, long short term memory.
1. Introduction
Despite its huge spread for now, urbanization does not stop to increase. This process results in
significant rise of concentration of population and human activity per square meter of territory in
relatively small space. High human activity leads to large-scale economic changes, as well as to large
emissions of heat, gases and waste, which as a result pollute air. Consequence of this process is
detected by harmful impact on human health [1].
The described processes are already typical not only for industrial cities, but also for urban centers,
where industrial production is not so highly influential. Analyzing the air quality index (AQI) in
different cities of the world, it is seen that there are not only industrial centers of the world among the
cities with low air quality. The list of top polluted cities also includes cities in Norway (Oslo), Poland
(Krakow), Croatia (Zagreb) [2]. In the context of Ukraine, it should be noted that the level of
pollution in Kyiv, which is the largest city of the country, currently prevails over industrial centers of
the country during some periods of time. All these factors prove that the huge number of people in the
world is affected by air pollution, and the problem of determining air quality level is widespread and
important.
Air pollution [3] is determined by concentration of particles and gases in the air [4]. Regarding the
problem of air pollution, it is important to monitor the current situation, analyze the accumulated data,
and to predict the future level of air pollution. Such a prediction is significant in short and long terms.
Prediction for medical diagnosis [5] is appropriate for the whole day or longer because of the specific
character of medical examination and decisions made on treatment. The paper is aimed at prediction
of air pollution during the next day.
IntelITSIS’2021: 2nd International Workshop on Intelligent Information Technologies and Systems of Information Security, March 24–26,
2021, Khmelnytskyi, Ukraine
EMAIL: vliovkin@gmail.com (V. Lovkin); olejnikaa@gmail.com (A. Oliinyk); lukashenkoyuriii@gmail.com (Y. Lukashenko)
ORCID: 0000-0002-6890-2807 (V. Lovkin); 0000-0002-6740-6078 (A. Oliinyk); 0000-0002-2478-4597 (Y. Lukashenko)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
At the same time the whole obtained, calculated and predicted dataset enables complex solution of
the problems of city management and medical diagnosis, because a man and a city, as well as
biosphere in general, are the main objects of the impact of air pollution in the result.
This paper is devoted to the consideration of air pollution in terms of creating a decision-making
framework for medical diagnosis, where prediction of the level of air pollution is actual for
determination of the individual impact of air pollution on the patient.
2. Air pollution prediction problem statement
Medical diagnosis mainly consists in determining the patient's diagnosis. The obtained diagnosis
becomes the basis for decisions to be made by doctor concerning further treatment of the patient. To
determine the diagnosis it is necessary to form a heterogeneous set of data which characterizes the
observed situation. On the one part this data is interlinked with subjective information about the
patient which is determined during survey and various types of examination and on the other part is
interlinked with the environment where patient lives.
The level of air pollution is one of the main indicators describing such an environment. Depending
on the environmental conditions, it is possible to plan the specification of medical examination, which
results are used in decision-making in diagnosis, and determine the specification of the
implementation of decisions made based on diagnosis results. The whole set of decisions [6] made
during a medical diagnosis forms a decision-making framework consisting of the following stages:
• making decisions concerning specification of the planned medical examination;
• making decisions concerning choice of diagnosis methods for the patient;
• making decisions concerning determination of the patient's condition;
• making decisions concerning the further treatment of the patient.
This group of decisions requires on the one hand the accumulation of historical data on air
pollution, i.e. indicators of air pollution by certain substances collected at the relevant stations, and on
the other hand prediction of the level of air pollution at these stations for the future.
The problem of air pollution prediction should be stated as determination of functional dependence
between air pollutant concentration level during the next period of time and its concentration level
during the previous periods of time together with additional parameter 𝜀𝜀:
𝑝𝑝𝑎𝑎𝑐𝑐 (𝑡𝑡, 𝑡𝑡 + 𝑛𝑛 − 1) = 𝑓𝑓(𝑝𝑝𝑎𝑎 (𝑡𝑡 − 1), 𝑝𝑝𝑎𝑎 (𝑡𝑡 − 2), … , 𝑝𝑝𝑎𝑎 (𝑡𝑡 − 𝑛𝑛), 𝑒𝑒(𝑡𝑡 − 𝑛𝑛, 𝑡𝑡 − 1)), (1)
𝑐𝑐
where 𝑝𝑝𝑎𝑎 (𝑡𝑡, 𝑡𝑡 + 𝑛𝑛 − 1) is an average level of concentration of air pollutant a which is calculated for
the time period which lasts from 𝑡𝑡 till 𝑡𝑡 + 𝑛𝑛 − 1, 𝑝𝑝𝑎𝑎 (𝑡𝑡 − 1), 𝑝𝑝𝑎𝑎 (𝑡𝑡 − 2), … , 𝑝𝑝𝑎𝑎 (𝑡𝑡 − 𝑛𝑛) present
concentration of air pollutant a at the discrete corresponding moments 𝑡𝑡 − 1, 𝑡𝑡 − 2, … , 𝑡𝑡 − 𝑛𝑛, f is a
functional dependence which has to be found in the study, 𝑒𝑒(𝑡𝑡 − 𝑛𝑛, 𝑡𝑡 − 1) is an additional parameter,
which presents additional factors, which influence 𝑝𝑝𝑎𝑎𝑐𝑐 , should be calculated for the time period which
lasts from 𝑡𝑡 − 𝑛𝑛 till 𝑡𝑡 − 1, and doesn’t depend on the corresponding air pollutant.
Each air pollutant a is an element of the set of air pollutants A, including gases and particulates.
This set should be created, including all air pollutants which are factors of decision making in medical
diagnosis.
The shape of functional dependence f should be investigated based on machine learning methods.
The main attention should be paid to recurrent neural networks and LSTM recurrent neural networks,
because of the nature of sequence 𝑝𝑝𝑎𝑎 (𝑡𝑡 − 1), 𝑝𝑝𝑎𝑎 (𝑡𝑡 − 2), … , 𝑝𝑝𝑎𝑎 (𝑡𝑡 − 𝑛𝑛) in the problem (1).
At the same time these discrete air pollutants form Air Quality Index (AQI), which is represented
by categorical value used for monitoring and decision making. AQI is obtained on the basis of levels
of the following pollutants:
• ground-level ozone (O3);
• particle pollution (PM2.5 and PM10);
• carbon monoxide (CO);
• sulfur dioxide (SO2);
• nitrogen dioxide (NO2) [7].
It means that accurate prediction of concentration of these pollutants is critical for accurate
prediction of AQI.
3. Related works
A number of studies concerning air pollution prediction using machine learning methods has been
conducted.
Concentration of PM2.5 is estimated in the study [8] using regression models. The proposed models
should be applied for countries where there is no possibility to use costly sensors to monitor air
pollution and to create dataset which is necessary for prediction by sequence-based methods.
Prediction is performed using real-time traffic monitoring based on Google Maps. Separate models
were built for different periods of day. Trace gas concentrations are observed only as additional data
for the environment where it could be accumulated. Regression models don’t enable to take into
account complex relations between data sequences and different types of factors which influence air
pollution.
Prediction of concentration of particulate matters using regression models was broadened out by
prediction of concentration of PM10 in the study [9]. Regression models were used to predict
concentration level during the next day.
Method for pattern analysis using dynamic time warping was proposed in the study [10]. This
method needs data on PM2.5 concentration from multiple stations and prediction is performed based
on similarity between stations. k-nearest neighbour method, which calculates dynamic time warping
as distance between stations by using its geographical coordinates, is used.
In the paper [11] support vector regression model was used to predict concentration of separate air
pollutants and to predict general pollution level based on the AQI. The following air pollutants were
studied: carbon monoxide, sulfur dioxide, nitrogen dioxide, ground-level ozone, particulate matter
2.5. Prediction was realized on an hourly basis. Appropriate results were obtained for O3, CO and
SO2, that’s why this approach couldn’t be recommended for universal usage.
The study [12] is dedicated to the relationship between air pollution and urban transport networks.
Artificial neural network model based on multilayer perceptron and the ARIMAX model are
compared using experimental investigation. Prediction is performed for an hour. It is proposed to use
ensemble model based on both models to process specific situations. Such an ensemble actually
models influence of transport network on nitrogen dioxide concentration in the city air, so it doesn’t
take into account other factors which influence on the air quality as well as other air pollutants.
Besides such a model does not consider sequences which exist in the history of air pollutant
concentration.
Deep learning model based on LSTM neural networks was investigated in the paper [13] where it
is presented in the context of Internet of Things concept [14, 15]. The proposed model is aimed at
AQI prediction, so the obtained results are categorical. During experimental investigation separate
LSTM models were created for ozone and nitrogen dioxide gases. The obtained results indicated that
sequence-based approach for air quality prediction is perspective and could be used in practice.
LSTM-based model is used in the paper [16] to predict PM2.5 concentration in the air of South
Korea. The prediction was performed for long-term periods. Different time horizons, including 8, 12,
16, 20, 24 hours, were investigated. It confirmed possibility to predict air pollution for intervals
longer than 1 hour.
In the paper [17] LSTM neural networks and deep autoencoders were used for PM concentration
prediction. PM2.5 and PM10 were investigated using datasets of Seoul. Prediction was performed for
10 days after period which was studied. During the experimental investigation LSTM models
demonstrated better results, therefore there is no practical need in the usage of deep autoencoders for
air pollution prediction problem.
The study [18] is aimed at road traffic prediction based on air pollution. CO, NO, NO2, NOx and
O3 are the observed air pollutants. Prediction was realized based on LSTM neural network
architecture. But at the same time air pollution is not a reason of road traffic but its consequence. So it
should be possible to improve air pollution prediction using road traffic data because road traffic is
one of the main reasons of polluted air in big cities.
The following study of the features which could be used for LSTM model to perform a prediction
in a day is needed. The problem of feature selection was considered in the studies of authors [19] and
should be applied to air pollution.
4. Proposed method of LSTM-based air pollution prediction using traffic data
Air pollution prediction is performed separately for each air pollutant, therefore prediction models
should be created for each air pollutant from the set A.
As a result of learning of air pollution nature and literature review the hypothesis on the
dependence between air pollution concentration and traffic data was moved. For further analysis of
this hypothesis separate investigation of correlation between city traffic data and concentration of air
pollutants was performed. The investigated correlation is positive. Its visualization is presented in the
Figure 1.
Figure 1: Graph of correlation between road traffic and concentration of air pollutants (CO, NO2,
PM10, SO2)
The problem (1) is solved using LSTM model [20, 21, 22, 23]. This decision was made because
input data are characterized by sequential nature, so this problem is a time series prediction problem.
The proposed structure of the model is presented in the Figure 2.
Figure 2: Structure of the model which is used for air pollutant prediction in the proposed method
The proposed model consists of two LSTM layers: the first layer of neurons interacts with input
data, the second layer is a hidden layer. The first layer is proposed to build from 8 neurons. Each input
neuron gets arithmetic mean value of concentration of air pollutant within 3 hours. Then obtained
values are processed by the hidden layer which consists of 2 LSTM-neurons. Amount of traffic
impacts on one of the neurons of the hidden layer, and hence impacts on the final prediction.
Dataset which is needed for the model training in the proposed method should be prepared in the
following way. Each sample represents values of parameters during a day and air pollutant
concentration for the following day registered in a separate station. Parameters include one value of
average day traffic in the region of station and 8 values of an air pollutant concentration during a day.
8 values were used instead of 24, because fluctuations of air pollutant within 3 hours are insignificant.
Air pollutant concentration during the following day is presented by median value calculated using 24
values of air pollutant concentration. The obtained dataset is normalized before model training.
Concentration data during the current day should be registered in one station and should be used
for prediction by the trained models which present each air pollutant in practice. Predictions are
performed by the models for the following day. The obtained values of concentration of each air
pollutant should be used for decisions on specificity of patient treatment.
5. Results
The main dataset [24], which was used for experimental investigation, represents air pollution
concentration levels registered during 18 years (from 2001 to 2018) in different stations in Madrid on
hourly basis. Approximately 150 thousands of measurements were performed for each air pollutant in
a station. Air pollutants presented in the dataset include SO2, CO, NO, NO2, PM2.5, PM10, NOx, O3,
TOL, BEN, EBE, MXY, PXY, OXY, TCH, CH4, NMHC [24]. Not all stations presented in this
dataset had measurements for full list of air pollutants which are included in AQI during full period
from 2001 to 2018: some measurements are missed because of the absence of some equipment, its
repair or unavailability. The example of missed (white color) and obtained (black) values is presented
in the Figure 3.
Figure 3: Missed values of concentration of air pollutants in the dataset under investigation
For experimental investigation only stations with equipment for registration of all air pollutants
from AQI were chosen. Missed values were replaced by averaging.
Traffic data for the chosen stations were obtained from Madrid's City Council Open Data website
[25].
The whole dataset was divided into learning sample (80 %) and test sample (20 %).
During experimental investigation the following models and methods were used for the problem
solving: ARIMA model, artificial neural network based on multilayer perceptron [22], vanilla
recurrent neural network [21], LSTM model [20], the proposed method which uses the same LSTM
model and traffic data as additional parameter. Software was developed for the investigation using
Python programming language. Keras library was used for neural network models realization.
For estimation of the obtained results metrics of root mean square error (RMSE) and mean
absolute error (MAE) were used:
𝑛𝑛 (2)
1
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = � �(𝐸𝐸𝑖𝑖 − 𝐴𝐴𝑖𝑖 )2 ,
𝑛𝑛
𝑖𝑖=1
𝑛𝑛
1 (3)
𝑀𝑀𝑀𝑀𝑀𝑀 = �⌊𝐸𝐸𝑖𝑖 − 𝐴𝐴𝑖𝑖 ⌋,
𝑛𝑛
𝑖𝑖=1
where 𝑛𝑛 is a number of samples in test dataset, 𝐴𝐴𝑖𝑖 is actual value (i-th sample from test dataset), 𝐸𝐸𝑖𝑖 is
a predicted value with number i.
The results of the conducted experimental investigation using these metrics were accumulated in
the Table 1.
Table 1
Comparison of RMSE and MAE values calculated for the existing models and the proposed method
for air pollutant O3
Prediction model/method RMSE MAE
ARIMA model 15.33 10.95
Multilayer perceptron 19.10 12.87
Recurrent neural network 14.95 10.63
LSTM 13.87 9.13
The proposed method 12.71 7.22
The obtained results demonstrate that LSTM model is characterized by better values of RMSE and
MAE than ARIMA model which is a classic solution for time series prediction problem, traditional
multilayer perceptron and vanilla neural network. ARIMA model allowed to obtain better results than
multilayer perceptron. At the same time additional usage of traffic data for model input allowed to
perform prediction with RMSE which is 9.13 % smaller and MAE which is 20.92 % smaller than
LSTM model without additional parameter.
Another metric was proposed to estimate accuracy of prediction of all air pollutant concentration
from AQI. This estimation was performed calculating percent of samples from dataset for which
prediction error was not larger than the limit (for example, 0.5 mcg/m3 for O3). The obtained results
are presented in the Table 2.
Table 2
Comparison of prediction accuracy for the test sample
Prediction model/method Accuracy, %
ARIMA model 86.35
Multilayer perceptron 83.96
Recurrent neural network 86.75
LSTM 88.62
The proposed method 90.98
The proposed method is characterized by the best results between the considered models and
methods. The accuracy of the proposed method is 4.63 % better than accuracy of ARIMA model and
2.36 % better than accuracy of LSTM model.
Prediction made for Arturo Soria station by LSTM model, created without traffic data and with
traffic data based on the procedure of the proposed method, is presented in the Figure 4. To accent the
differences between character of values of the previous day and the next day for which prediction is
performed, predictions are visualized with a day interval.
Figure 4: Predicted values of O3 concentration in Arturo Soria station
6. Conclusion
Air pollution prediction problem is considered from the point of view of decision making in
medical diagnosis. Main features of such decisions within the decision making framework are
presented.
Mathematical formalization of the air pollution prediction problem is made. Method of the
problem solution is presented. Prediction model in the method is organized using LSTM neural
network and consists of 2 LSTM layers. Road traffic data is used for additional presentation of
environment as a factor which impacts on air pollution. Data preparation procedure is described in the
paper.
Experimental investigation of the proposed method is performed using dataset collected in Madrid
during 18 years. ARIMA model, multilayer perceptron, vanilla recurrent neural network and LSTM
are used as alternatives. The model, which was trained according to the proposed method, allowed to
obtain better results, including smaller values of RMSE, MAE and better accuracy level.
The proposed method should be used in practice inside medical diagnosis tools and separate
systems for air pollution analysis, enabling to predict air pollutant concentration level during the next
day.
7. Acknowledgments
The work was performed as part of the research work "Development of methods and tools for
analysis and prediction of dynamic behavior of nonlinear objects" (state registration number
0121U107499) of Software Tools Department of National University “Zaporizhzhia Polytechnic”.
We are particularly grateful for the assistance with data sample which was given by Diego
Vicente, Junior Data Scientist at Decide Soluciones in Madrid, Spain.
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