Final Synopsis -Bharathi(21-4-23).doc

Synopsis of the Thesis
An Intelligent Framework for Air Pollution Monitoring
System using Internet of Things and Machine Learning
Synopsis submitted to the GITAM (Deemed to be University)
In Partial fulfillment of the requirements for award of Degree
of Doctor of Philosophy in Computer Science
R. UDAYA BHARATHI
[Regd. no.1263715404]
Department of Computer Science
GITAM School of Science
GITAM (Deemed to be University)
Visakhapatnam
April 2023

Abstract:
Air pollution has started to endanger human life in many countries throughout the world due
to human activity, industry, and urbanization during the past few decades. Particulate
Matter2.5(PM2.5) is one of the most hazardous air pollutants of all the Particulate Matters that
affect air quality, posing a serious risk to both human health and the environment. In
addition to contributing to global warming and the greenhouse effect, it also worsens
respiratory conditions including asthma and lung cancer. According to the World Health
Organization (WHO), poor air quality affects more than 80% of individuals in urban areas
and 98% of cities in low and middle-income nations. The primary goal of the thesis is to
promote public knowledge about air quality and the elements that contribute to air pollution,
as well as to provide tools for measuring and analyzing air quality. Various air pollutants
and their causes are discussed in the thesis. To control air pollution, it is essential to
constantly forecast air quality. The air quality index (AQI) is a metric for measuring the
quantity of air pollution. Different deep learning systems can aid in AQI forecasting.
Internet of Things (IoT) and various deep learning techniques like Long Short-Term
Memory (LSTM), Recurrent Neural Networks (RNN), Gated Recurrent Unit (GRU) and
Convolutional Neural Networks (CNN) are used in this thesis to predict the air quality index
in an efficient manner. The main objective of the thesis is to build and train techniques using
IoT and deep learning algorithms and predict the most accurate model in predicting the air
quality index by comparing the results with other baseline techniques. Experimental results
for the used algorithms are provided that indicate more accurate prediction models. To
determine the optimum model performance, three assessment measures were used,
including determination coefficient (r2), Mean Square Error (MSE), and Root Mean Square
Error (RMSE). A comparison of the suggested system’s performance with the existing
method demonstrates its efficiency and performance enhancement in terms of error.
Keywords: Air pollution, deep learning, AQI, pollution detection, IoT

I.INTRODUCTION
Air pollution has become a life-threatening concern in many countries throughout the world
in recent decades as a result of human activity, industrialization, and urbanization. Among
the many dangers posed by polluted environments, air pollution ranks high. Given that
every single living thing constantly necessitates pure, high-quality air to survive. Without
this air, no living thing could possibly survive. However, air is becoming increasingly
polluted as a result of vehicles, farms, factories, mines, and the burning of fossil fuels [1].
These processes release toxic air pollutants such Sulphur dioxide, nitrogen dioxide, carbon
monoxide, and particulate matter into the atmosphere. As a result, there is a lookout for
cutting-edge methods of air pollution forecasting [2]. Thus, data-mining techniques are used
to foretell the presence of air pollution in the smart city. This model incorporates the
random forest method into a multivariate, multistage time series data mining process. To
forecast air quality, it incorporates historical and real-time data into a model. [3] This
approach simplifies things, works better, and is more realistically applicable, so it can help
smart city environmental protection agencies make better decisions. The goal of the
suggested model is to provide a framework for automated air quality monitoring and
forecasting, which successfully gives the upcoming air quality of that specific location and
alerts users in the case of significant air pollution. Human health and government
policymaking both stand to benefit greatly from a reliable system for monitoring and
forecasting air pollution levels in advance. Artificial Intelligence and its derivatives are just
one example of the many Machine Learning and deep-learning algorithms that have
emerged in tandem with the rise of AI [4]. Therefore, the purpose of this research work is to
conduct a study on the use of cutting-edge research for effective monitoring systems. This
study seeks to compare the outcomes of different methods in order to better understand how
they may be used to predict Air Quality [5]. Additionally, the objective of the research is to
develop forecasting model with high accuracy using weather data and the density of nearby
stations that generate huge dataset.
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II. OBJECTIVES AND SCOPE OF THE RESEARCH
WORK
Air monitoring and protection have emerged as pressing issues in densely populated
urban areas. Increase in population leads to higher demand for transportation, power,
and fuel. Heavy air pollution poses a significant risk to all forms of life on Earth. This
means that assessing and monitoring air quality is essential, and the government should
act accordingly. Hence a framework is developed to enhance the quality of Air
predictions through cutting edge research monitoring mechanisms.
Scope of the research
Effective utilization of Internet of Things associated with various Machine Learning
frameworks would pre-determine the quality of air and thus take effective
mechanisms to control the dangerous hazards in contaminating air quality. The Scope
of the research has been extended to deliver frameworks for predicting air quality and
take severe actions upon reducing the risks on Human Welfare.
Objectives of the Proposed Research
• To Study the potential levels of gaseous pollutants in the environment and
Climatic changes using Intelligent Air Prediction system using Internet of
Things.
• To focus on the construction of framework based on Time Series Augmentation,
which integrates Deep Learning Techniques like Vector Auto Regression and
Long Short-Term Memory, for Effective Air Quality Prediction.
• To predict air pollution using Gabor Transformation and Convolutional Neural
Network associated with Deep Image classifiers upon sky datasets for
classifying air pollutants levels of the same city on different days.
• To build a hybrid model of image Analysis based on VAR-LSTM method of air
quality prediction.
• To use an Intelligent Monitoring system to predict Air Pollutants in Indian
Cities by Calculating moving Averages with standard metrics.
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III. WORK CARRIED OUT
Traditional Approaches involved in determining the quality of air and predicting its
impact on society has been failed due to unreliable frameworks, which deals with
large amount of data and poor conventional mechanisms. To overcome this research
study have proposed an Intelligent framework for Smart Monitoring system to predict
the quality of air.
There has been a proposal for an Internet of Things-based air pollution monitoring
system that enables real-time and ongoing monitoring of pollutant levels. When
compared to a traditional monitoring station, the construction, operating principle, and
advantages of this smart monitoring system are recognized. The develoent of IoT and
integrating Machine Learning has a tremendous impact on smart city applications,
cloud computing, and air quality monitoring systems. Portable air monitoring systems
are anticipated to see widespread market adoption in the future.
1. Intelligent Air Prediction System Using Internet of Things
Firstly, an IOT based framework titled “Intelligent Air Prediction System Using
Internet of Things” is used to monitor and natural air pollution, an Internet of Things
foundational structure is suggested. This framework can be used to assess the air
quality through a mobile application, examine air contamination in a particular area,
and observe air contamination. It makes use of an Arduino in conjunction with
individual gas sensors, such as those for carbon monoxide, alkali, particle matter,
moisture, and smoke, to estimate the grouping of each gas separately. The below
figure describes the architecture of proposed framework.
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Air
Gas Sensor
Buzzer
Wi-Fi
ARDUNIO
UNO
(Microcontroller)
Power Supply
Web Browser
Mobile App/
LCD Display
Serial
Figure 1: Framework for Intelligent Air Prediction System using Internet of
Things
Most commonly, an open-source develoent board called the Arduino UNO that uses
ESP8266-12E chips is utilized [8]. The MQ135 gas sensor is used to monitor gas
concentrations, and the Arduino would use the sensor data it would receive to gather
information for the Internet of Things (IoT). An Arduino board is linked to a Wi-Fi
gadget and a gas sensor. Moreover, data can be shown on a Liquid Crystal Display
(LCD) or mobile application that is connected to the Arduino board. The user will
monitor the air quality using a serial monitor and LCD and will sound an alert when
the air quality falls below a certain threshold, which happens when enough hazardous
gases, such as CO2, smoke, alcohol, benzene, and NH3, are present in the air. Both
the LCD and the serial monitor show the air quality in Parts Per Million (P) so that
users may easily check it. The MQ135 sensor is ideal option for monitoring air quality
because it can reliably count how many dangerous gases are present and can detect the
majority of them.[9]. These algorithms include linear regression, random forest, XG
boost, and ARIMA. The dataset used in the above model is central pollution control
board. http://app.cpcbccr.com/AQI_India
4

BEGIN
MQ 135 Gas Sensor & ESP8266 WIFI
connection establish to Arduino
Yes
No
Check Sensor &
Wi-Fi connection
Yes
Perform Sensor Data analytic.
Reset Connection
Yes
Air Quality Value
Display ‘Pure Air Quality’
<0.5 p
No
Value >0.5 &
Yes
Display ‘Impure Air
< 1p Quality’
No
Yes
Value >1 p Display ‘Harmful’
END
Figure 2: Working Process of the Proposed Model
Conclusion: The framework that was built would allow for monitoring the air quality
with the help of Internet of Things devices as part of the work on an air pollution
check. This information is delivered to the microcontroller by air sensors in the
framework. Data for the web server is then provided by the microcontroller. The next
task is to predict air quality.
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2. Time Series Augmentation based on Vector Auto Regression and
Long Short-Term Memory method for Air Quality Prediction.
Secondly, this research study is extended towards “Time Series Augmentation
based on Vector Auto Regression and Long Short-Term Memory method for Air
Quality Prediction”, Using the air quality forecast based on deep learning and the
Autoregressive Integrated Moving Average (ARIMA) model is one of the many
known methodologies [9]. Vanishing gradient issues and unpredictable performance
in prediction are limitations of current techniques. To enhance the performance of the
Air Quality Index, a hybrid strategy using Vector Auto Regression (VAR) and Long
Short-Term Memory (LSTM) is proposed in this study. [11] The VAR model
normalizes the data based on the properties of multivariate data and enhances the data
to make is extended upon focusing on the Long Short-Term Memory (LSTM)
approach and the Vector Auto Regression (VAR) method are combined to increase
the performance of the Air Quality Index. The Central Pollution Control Board
(CPCB), which is open to the public, provides the data on Indian air quality.
(https://cpcb.nic.in/). Due to its ability to process data sequentially and store key
properties for a long period, the LSTM model is suited for time series analysis and
prediction.[10] The VAR model normalizes the data based on the features of the
multivariate data and enhances the data to make the data acceptable for the LSTM
training. The proposed methodology is depicted in the below figure.
Input Vector Auto
Air Quality
Dataset Regression
Prediction
(CPCB) & LSTM
Figure 3: Block Diagram of Vector Auto Regression & Long Short-Term
Memory Model.
In the monitored station, the CPCB collects data on the air quality and other relevant
variables. The VAR model is used in this study to perform normalization and augment to
prepare input data for classification. In order to forecast air quality, extended data was
used using the LSTM model. The basic idea behind the suggested approach is to
normalize and supplement the input time-series data of pollutants using the VAR
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model [13]. Using it later to train the LSTM model and improve AQI Value
prediction with less information loss.
Factors affecting air quality are intricate, and there are dynamic interactions between
the aspects. The general simultaneous equations model is less effective at uncovering
dynamic effects when investigating lag phase effects of explanatory variables on their
own [14]. The simultaneous equations that are currently available have set variables
that are exogenous or endogenous variables that exclude some crucial lag factors. The
VAR model reduces subjective settings in model error by treating all variables as
endogenous. The VAR model provides the following benefits over the conventional
single Equation (1) universality, ease of adding explanatory variables, and lack of
theoretical foundation. Equation (2) The long- and short-term relationships between
the air quality parameters are revealed using the VAR model.
Eq. (1) provides the formula for the VAR model.
= + 1 −1+⋯+− + =0,±1,±2 (1)
Where random vector ( × 1) is of ( × 1) vector is denoted as = ( 1, ... ,
)′.
in
= ( 1 , ..., ) ′, coefficient matrix of ( × ) is denoted as , intercept terms.
The random error term of K-dimensional is denoted as = ( 1 , ... , )′, and
classic econometric assumptions are given as
( ,
′
)=0 ( ≠) and ( )=0, ( ,
′
)= 2
A non-singular matrix is represented by the symbol 2 if there is no further assertion.
The augmented and normalized values of the pollutant data make up the resultant
vectors of the VAR. To increase learning rate and lower prediction errors, these
parameters are sent into the LSTM. LSTM is chosen as the primary algorithm for
estimating 2.5 concentration in this work after careful consideration.

Ct-1
ft
S
Yt-1
it
S
Cat
tanh
Ct
tanh
Ot
S
Yt
Xt
Figure 4: Architecture of LSTM Gate Functioning.
The LSTM input gate function is depicted in Figure 4 to upgrade the cell state, the
input gate does the subsequent processes. The logistic layer and tanh layer form the
input gate layer. tanh layer provides candidate values ( ) as in Equation (1.1) and the
sigmoid/logistic layer determines which value will be updated as in Equation (1.2).
=[ −1++ ] (1.1)
= [ −1 ++ ] (1.2)
As shown in Figure, the forget gate decides what information must be remembered
and what can be forgotten. The sigmoid function accepts information from the hidden
state Y_ and the current input Xt (t-1). The Sigmoid algorithm generates numbers
between 0 and 1. It assesses if some of the preceding output is necessary by producing
an output that is closer to 1. The cell for step multiplication will eventually use this
value of f t. The forget gate function is shown in the equation below.
= [ −1++ ] (1.3)
Where,
- weighted matrix between forget gate and input
gate - connection bias.
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There are various types of gates involved in the complete framework which have been
discussed in detail. The detailed working phases of LSTM training is described in the
below table.
Phase Characterization
1st
Phase • Pre-processing of various pollutants.
• Examine, analyse, and purge the dataset.
• Set the look back and normalize the dataset.
• LSTM training.
2nd
Phase • Create an LSTM network with a single input, four hidden
layers, and a single value prediction layer.
• For the LSTM layer, use the sigmoid function.
3rd
Phase • Train the network using 48 epochs and a 24-batch size.
• Using the training model, get predictions for the test
dataset.
Table:1 Phases of LSTM training.
The below figure represents the prediction error from the mean square error, reduced
mean square error, and determination coefficient. In terms of predicting contaminants,
LSTM surpasses CNN, RNN, and GRU methods [15]. Yet, in some situations,
alternative algorithms outperform LSTM. In the prediction of NO2, a gated recurrent
unit offers a greater determination coefficient than CNN, RNN, and LSTM. RNN
produces the least amount of error when compared to other algorithms for SO2
prediction. Three evaluation metrics were utilized to select the best model
performance- the determination coefficient (R2
), Mean Square Error (MSE), and Root
Mean Square Error (RMSE).
Data preprocessing is an essential stage in every machine learning and deep learning
process since it affects the algorithm's capacity to generalize. There will be a number
of stages in execution, including hidden layers, because neural networks are being
used in the study. The procedure will be simplified by requiring less inputs,
processing time, and attribute count. Managing data outside the detection range
(negative value), imputed missing data, outlier identification, and data transformation
are a few examples of data preprocessing techniques used in this study. The first two
9

strategies will assist in producing more accurate and comprehensive data sets, while
the third method will produce more evenly dispersed data and less variability [16]. To
obtain a new data set with more information, the fifth phase will then be used. In
many cases, feature extraction and feature selection are the last steps in data
preparation.
Air Pollution Dataset
Data Preprocessing
Data Splitting
Training dataset Test dataset
Sequence modelling
LSTM Network Model LSTM Network with
timesteps
Model Prediction
Compare Prediction Result
Figure 5: Flow chart of Predictions through LSTM.
Accurate air pollution forecasting encourages people to live healthy lives and the
Government to take the necessary action to reduce pollution. In this chapter, LSTM,
CNN, RNN, and GRU models based on the data are used to forecast 2.5, NO2, CO,
and SO2 levels. One-hour intervals are used to collect the data throughout a twenty-
four-hour period. For the training set employed in this study, the ideal model
10

parameters, such as 48 epochs and a 24-batch size, are established.[17] The
performance comparison of all the prediction models reveals that the LSTM model
performs better than other models. In terms of optimum model performance, the
LSTM's predicted result is identical to the real one and has a high degree of accuracy
and a low rate of error. Despite rising air pollution levels, India still lacks enough
monitoring sites to properly estimate the country's Particulate Matter levels.
IV. RESULTS AND DISCUSSION
Evaluation NO2 CO SO2 2.5
Models r2
MSE RMSE r2
MSE RMSE r2
MSE RMSE r2
MSE RMSE
CNN 0.435 0.098 0.180 0.005 0.032 0.155 0.055 0.034 0.154 0.011 0.169 0.265
RNN 0.010 0.110 0.124 0.017 0.056 0.076 0.119 0.009 0.146 0.241 0.310 0.194
LSTM 0.521 0.051 0.056 0.103 0.007 0.054 0.277 0.076 0.065 0.992 0.044 0. 112
GRU 0.761 0.076 0.147 0.088 0.164 0.075 0.098 0.073 0.181 0.728 0.581 0.153
Table 2: Prediction Error from the Coefficient of Determination, MSE, and
RMSE.
The LSTM technique provides a minimal error rate and the best coefficient of
determination. The average performance comparisons of three assessment measures
are plotted in the below graphs.
1.2
1
0.8
0.6
0.4
0.2
0
CNN
RNN
LSTM
GRU
No2 CO SO2 PM2.5
Figure 6: Average Performance in terms of r
2
with Different Pollutants
The above graph demonstrates the average performance comparison for NO2, CO,
SO2, and 2 contaminants using the determination coefficient (r2). For all pollutants
except NO2, the determination coefficient using the LSTM prediction model yields
the best results compared to other techniques. In this system, GRU is marginally
bigger than LSTM. Overall, as demonstrated in the example above, LSTM is the more
accurate prediction model.
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The below figure shows the average performance comparison of the pollutants NO2,
CO, SO2, and 2.5 in terms of mean square error. In SO2, LSTM and GRU perform
similarly, while RNN has a lower MS error rate than other prediction models [18]. For
all pollutants except SO2, the results of MSE using the LSTM prediction model
demonstrate the least amount of error compared to other techniques. Overall, the
LSTM prediction model outperforms other gases and our primary prediction of
particulate matter 2.5.
0.8
0.6
0.4
0.2
0
CNN
RNN
LSTM
GRU
No2 CO SO2 PM2.5
Figure 7: Average Performance in terms of MSE with Different Pollutants
When the total average r2
, MSE, and RMSE prediction for all pollutants were also
assessed, the LSTM model beat all other models. The comparison demonstrates that
the suggested method is capable of making the most accurate predictions between
LSTM and GRU models. Yet, LSTM and RNN algorithms with a longer time horizon
may provide the maximum accuracy in predicting air pollution concentrations (48
hours). Of all the sources of pollution, the LSTM neural network was the most
accurate in predicting the 2.5 concentration level. The next part of the research is to
detect air pollution using sky images.
3. Air Pollution Detection from Sky Images with Deep Classifiers
Thirdly, the suggested method “Air Pollution Detection from Sky Images with
Deep Classifiers” uses the camera of a smart phone to calculate the level of local air
pollution. Tehran is covered with a massive collection of photographs. The severity of
air pollution is then assessed using a pair of methods. The first method, Gabor
transform is used to extract features from the pre-processed images. Finally, the level
of air pollution is estimated and anticipated using two straightforward categorization
algorithms. This method uses an image of the sky as an input and calculates an air
pollution index based on the image's quality. A convolutional neural network (CNN)
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is used to achieve this. A lot of research has been done on the suggested method, and
the findings support the idea that the technology can reasonably predict air pollution
levels. The results of conventional feature extraction and classification techniques
have been improved by roughly 10%, and the deep classifier has an accuracy rate of
90% or higher. The picture below, which is updated regularly, provides a detailed
description of the many phases of the proposed model.
Training Phase
Images Dataset
Data Acquistion
Preprocessing
Feature Extraction
Building a
Classification model
unified Model
Figure 8: Training Phase of air pollution detection using Deep Classifiers
To accomplish this, it is required to access a sizable database of photos shot on
occasions when air pollution levels varied. In this context, a 5-M pixel camera is used
to collect images from five locations in Tehran between 18 October 2016 and May 31,
2017. The camera's location wasn't fixed. But the goal was to take pictures at specific
locations in the streets. As a result, images may have a slightly different horizon line
and field of view. These areas were chosen because they are typical of urban
environments and have high levels of air pollution from the numerous cars that are
present there. At 8 AM and 11 AM every day, the Municipality of Tehran provides a
report on the air quality readings in these places. The information on the air pollution
levels of various locations on various days is obtained from the website
www.air.tehran.ir.
The Municipality of Tehran graded the air quality on a scale of "excellent" to "good,"
"lightly polluted," "moderately polluted," and "heavily polluted." It is shown in the
Table below. Every day and for each area, the municipality announces the air
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pollution level based on the five recommended thresholds. The pictures from this
collection are therefore used in each example in accordance with the Municipality's
statement for each area. Every day in Tehran, the Municipality distributes the
information for certain sites. The dataset's 482 photos have been used. The number of
photos taken at varying levels of air pollution is displayed in Table.
Air quality Excellent Good Lightly Moderately Heavily
Level Polluted Polluted polluted
Number of 100 256 88 38 0
Images
Table 3.: Number of instance classes acquired from dataset.
Result and Discussion of air pollution detection using Deep Classifiers
The suggested CNN has seven layers, including three convolutional layers, two
scaling layers, and two completely linked layers. In order to accelerate the learning
processes, Rectified Linear Unit (ReLU’s) are employed to perform a linear
transformation followed by a nonlinear guidance in each layer. Conjecture is
improved when the first two tiers are exposed to neighborhood bring response criteria.
To achieve translational invariance, max pooling is applied on all convolutional layers
except the third layer. The network receives warped RGB picture patches of size
200X 200.
Method description Accuracy F1
Precisio
n Recall
K=1 0.88 0.84
0.87
0.88
K=15 0.88 0.84
0.88
0.88
K=20 0.88 0.84 0.88 0.88
Feature selection &
0.88 0.88 0.87
0.88
k=10
Feature selection &
0.89 0.86 0.86 0.89
k=15
Feature selection &
0.89 0.86
0.86
0.89
k=20
Table 4: Classification Accuracy integrating KNN method.
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Second method shows the outcomes of evaluating the precision of several CNN
models' predictions of locations. Convolutional channel size, scaling factor, layer
grouping, number of element mappings, and layer count can all be altered. The
strongest produced result is shown in Table 5, which is positive. Table 5's findings
demonstrate that Line 8's develoent produced the highest level of precision. The
number of element mappings has a more significant influence on the display than the
scale layers do. It also doesn't matter how large the convolutional channel is.
No. Layers Accuracy
1 C(10)(5)-S(2)- C(10)(7)-S(2)- C(10)(5)-S(2)- C(4)(7) 86.25%
2 C(10)(5)-S(2)- C(10)(7)-S(2)- C(10)(5)-S(2)- C(2)(7) 78.75%
3 C(10)(5)-S(2)- C(10)(7)-S(2)- C(10)(5)-S(2)- C(10)(7) 56.88%
4 C(10)(5)-S(2)- C(10)(7)-S(2)- C(10)(5)-S(2) 88.13%
5 C(4)(5)-S(2)- C(6)(7)-S(2)- C(10)(5)-S(2) 89.75%
6 C(4)(5)-S(2)- C(6)(5)-S(2)- C(10)(5) 84.50%
7 C(4)(5)-S(2)- C(6)(5)-S(2)- C(7)(5) 82.25%
8 C(6)(5)-S(2)- C(6)(5)-S(2)- C(6)(5) 93.38%
9 C(6)(5)-S(2)- C(6)(5)-S(2)- C(5)(5) 84.75%
10 C(6)(5)-S(2)- C(6)(5)-S(2)- C(4)(5) 88.25%
Table 5: Classification accuracy of the second proposed method (CNN) on
prediction the air pollution level.
We may determine the texture of an image by applying various filters and
modifications. This study is possibly the earliest attempt to end user to measure air
pollution from an image, using the Gabor channel for include extraction and the KNN
and Random Forest order computations for showing. The second method involves
sending the unprofessional photographs to CNN for analysis. The outcomes of several
tests designed to evaluate the proposed systems have been provided. This study
presents a logical CNN framework [19]. In the end, the convolutional layer's output
might be fed into a layer like ReLU that has optional enactment capabilities. The
dropout approach can be used to obtain better results and prevent over-fitting.
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4. Image Analysis Based on Var-LSTM Method for Air Quality
Prediction
Finally, this research study is extended for estimating Particulate Matter () air
pollution based on an analysis of many publicly available images of the surrounds of
Beijing, Shanghai (China), and Phoenix (US). Six elements were extracted from the
photographs through processing in order to forecast the 2.5 index in conjunction with
other factors including the time of day, location, and weather. This was accomplished
utilizing deep learning techniques, notably the training of a VAR-LSTM model with
the previously described photo dataset. The results show that it is possible to estimate
2.5 using the image analysis technique. The Proposed Method has the following
phases and depicts in the above figure as follows.
Data
Vector Long Short
Combining
ROI Feature Auto Term
Acquistion VAR &
Selection Extraction Regression Memory
(Images) LSTM
(V.A.R) (L.S.T.M)
Figure 9: Phases of Proposed Model Utilizing Images in Combination with VAR
& LSTM.
VAR Model: The short- and long-term relationships between the air quality
parameters are revealed by the VAR model. The VAR models have drawbacks, such
as the need to measure a large number of parameters and the high correlation in the
lag times of the explanatory variables.[20] The research demonstrates that there are
numerous intricate links between the factors that contribute to CO2 emissions and the
emissions themselves. The driving power of CO2 emissions is analyzed dynamically
using the VAR model.
Below Equation provides the formula for the VAR model
= + 1 −1+⋯...........+ − + , =0,1,2 where random vector ( × 1) is in
= ( 1 , ... , )′ , coefficient matrix of ( × ) is
denoted as , intercept terms of ( × 1) vector is denoted as = ( 1, ... , )′.
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LSTM Model: Based on the cell and forget gate, the LSTM may permanently keep
the crucial information. The LSTM model has proven to be helpful since it can deal
with the challenges of long-term dependencies utilizing a self-feedback mechanism
that acts on a hidden layer. In order to address the issue of long-term features, a
memory cell and three gates, such as input, forget, and output gates, were utilized to
retain information.
Combining VAR & LSTM: This technique uses a two-step training procedure. The
LSTM model and VAR fitted values are used to forecast in a series of one step. The
same differential data used for VAR fitting is used for training. In order to encode
cyclically, [16] LSTM handles external data sources such as weather forecasts or
properties like months, hours, and workdays. A neural network improves performance
on test data by learning from two independent data sources. Multi-step training is
required to solve the Vanishing Gradient Problem. A neural network will forget the
first task it was given if it is given two tasks, which is a common issue with neural
networks.
Dataset RMSE R-squared
F-test
Beijing 43.62 0.64 P<0.0001
Shanghai 20.23 0.57 P<0.0001
Phoenix 2.34 0.23 P<0.0001
Table 6: Assessment of VAR & LSTM
Conclusion: An image-based methodology was used to assess the 2.5 index in the
atmosphere. Comprehensive analyses of a variety of image properties, including
transmission, picture difference and entropy, sky perfection and variation, were
conducted using detailed 2.5 data for Beijing, Shanghai, and Phoenix. The method
could provide a reasonable expectation of 2.5 file across a wide 2.5 list range,
according to researchers in Beijing (327 images, one for each day of the study's 327-
day duration), Shanghai (1954 images, or 8 images per day for 245 days), and
Phoenix (4306 images, or 16 images per day for 270 days).
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5. Air Pollution Detection and Prediction Using Moving Average in
Indian Cities
Lastly the research study is focused upon the machine learning-based prediction
technologies have been shown to be more effective than conventional methods for
researching these contemporary threats. The current study analyses and predicts air
quality using six years' worth of air pollution data from Indian cities. The dataset has
undergone thorough preprocessing, and the correlation analysis has been used to
identify essential features. The removal of surplus gases like carbon dioxide and other
vapors is insufficiently accomplished by the carbon cycle and the nitrogen cycle. The
major Indian and American cities' statistics from January 2019 to May 2021 are
included in the Air Quality Index dataset. Better outcomes are produced by data
analysis using the Moving Average Prediction Model (MAV). The results for the air
quality index and 2.5 are based on forecast and estimation. An exploratory data
analysis is utilised to get insights into the dataset's underlying trends and pinpoint the
pollutants that directly impact the air quality index. The outputs from these models are
compared to the widely used metrics.
Air Quality Index (AQI) Calculation in Proposed Model:
All nations use the same criteria to evaluate the state of the atmosphere on Earth.
Other contaminants that are monitored in India include lead and ammonia. An
acceptable level of air quality is one with an AQI less than 50. The dataset includes
information for significant Indian and American cities for the time period of January
2019 to May 2021[20]. Geo pandas will be used to plot the shape file for the India
map, which is called
Shape Geo. Lat Long is the information for a city's latitude and longitude that will aid
in our map - plotting. Although the dataset includes data from both India and the US,
it focuses on Indian data and uses the US dataset for comparison.
18

Table 7: AQI category, pollutants and Health break point
Figure 10: Air quality index of 2019-2021 from US and India
Conclusion: To reduce the escalating levels of air pollution, the government must
also establish emission guidelines and implement legislation rules The Indian
government has implemented stringent measures, placing sensors and stations across
the nation to track the levels of pollution in the areas that are most severely affected.
To combat pollution, the government has implemented a number of policies, such as
extending metro facilities for public transit and establishing laws requiring private
vehicles to have number plates with odd numbers. In this regard, the Indian
government initiated a campaign against dust, and each department was tasked with
creating its own anti-dust cell. To lessen air pollution, the Graded Reaction Action
Plan (GRAP) would be implemented. The use of firecrackers and fire burning on the
eve of Diwali and other holidays is prohibited, and anyone found in violation will face
a six-year sentence in prison, as per the Prevention and Control of Pollution Act.
These techniques will be rendered ineffective if pollution continues to rise at the
current rate.
19

IV. CONCLUSION & FUTURE SCOPE
A feature with a stronger correlation coefficient with the 2.5, weather information,
and correlation with other stations was chosen after experimental comparison. To
successfully extract the spatial characteristics of and internal characteristics of various
variables based on the suggested hybrid model, which employed CNN. At the same
time, LSTM was used to acquire the time features and obtain a more precise and
stable prediction result. The key conclusions of this study are as follows based on
performance evaluation and results comparison, the designed models can efficiently
extract the temporal and spatial aspects of the data through CNN and LSTM, and it
also has high accuracy and stability.
V. REFERENCES:
1. Sharma, N., Agarwal, A. K., Eastwood, P., Gupta, T., & Singh, A. P. (2018). Introduction to air
pollution and its control. In Air Pollution and Control (pp. 3-7). Springer, Singapore.
2. Choudhary, M. P., & Garg, V. (2013, August). Causes, consequences and control of air
pollution. In All India Seminar on Methodologies for Air Pollution Control, held at MNIT.
3. Haque, M. S., & Singh, R. B. (2017). Air pollution and human health in Kolkata, India: A case
study. Climate, 5(4), 77.
4. Sarker, I. H. (2021). Deep learning: a comprehensive overview on techniques, taxonomy,
applications and research directions. SN Computer Science, 2(6), 1-20.
5. Dhingra, S., Madda, R. B., Gandomi, A. H., Patan, R., & Daneshmand, M. (2019). Internet of
Things mobile–air pollution monitoring system (IoT-Mobair). IEEE Internet of Things Journal,
6(3), 5577-5584.
6. Bekkar, A., Hssina, B., Douzi, S., & Douzi, K. (2021). Air-pollution prediction in smart city,
deep learning approach. Journal of big Data, 8(1), 1-21.
7. Sonar, H., Kagne, V., & Khalane et.al Analysis and prediction of air quality in Nanjing from
autumn 2018 to summer 2019 using PCR–SVR–ARMA combined model. Scientific reports,
11(1), 1-14.
8. Haque, M. S., & Singh, R. B. (2017). Air pollution and human health in Kolkata, India: A case
study. Climate, 5(4), 77.
20

applications and research directions. SN Computer Science, 2(6), 1-20.
10. Dhingra, S., Madda, R. B., Gandomi, A. H., Patan, R., & Daneshmand, M. (2019). Internet of
Things mobile–air pollution monitoring system (IoT-Mobair). IEEE Internet of Things Journal,
6(3), 5577-5584.
11. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of
things: A survey on enabling technologies, protocols, and applications. IEEE communications
surveys & tutorials, 17(4), 2347-2376.
12. [3] Dizdarević, J., Carpio, F., Jukan, A., & Masip-Bruin, X. (2019). A survey of communication
protocols for internet of things and related challenges of fog and cloud computing integration.
ACM Computing Surveys (CSUR), 51(6), 1-29.[4] Kim, T. H., Ramos, C., & Mohammed, S.
(2022). Smart city and IoT. Future Generation Computer Systems, 76, 159-162.Rajab, H., &
Cinkelr, T. (2018, June). IoT based smart cities. In 2018 international sym Medved, D. (2018).
Deep Learning Applications for Biomedical Data and Natural Language Processing. Department
of Computer Science, Lund University.
applications and research directions. SN Computer Science, 2(6), 420.
14. Tejasri, N., & Ekapanyapong, M. (2019). Material Recognition Using Deep Learning
Techniques.
15. Roman Cardell, J. (2020). Python-based Deep-Learning methods for energy consumption
forecasting (Bachelor's thesis, Universitat Politècnica de Catalunya).
16. Maggiolo, M., & Spanakis, G. (2019) Autoregressive convolutional recurrent neural network for
univariate and multivariate time series prediction. arXiv preprint arXiv:1903.02540.
17. symposium on networks, computers and communications (ISNCC) (pp. 1-4). IEEE.
18. Kök, I., Şimşek, M. U., & Özdemir et.al Leandro, L., & Mueller, D. (2020). A gated recurrent
units (gru)-based model for early detection of soybean sudden death syndrome through time-
series satellite imagery. Remote Sensing, 12(21), 3621
21

VI. RESEARCH CONTRIBUTIONS:
Sl ISSN Impact
No. TITLE Publications factor
International Journal of 2395-6011 May
1. Internet of ThingsTrends and surprises Scientific Research in Science vary in
and Technology (2020)
5.3(201
7)
Weather and Air pollution real- time International Journal of 2278-3075 1.0
2. monitoring system Innovative Technology and
using Internet of Things Exploring Engineering
(IJITEE)
Intelligent Air Pollution Prediction systemInternational Journal of 2249-8958 1.0
3. usingInternet of Things. Engineering and Advanced
Technology (IJEAT)
4. Time Series Augmentation based on International Journal of 0974-5823 1.04
Multivariate Sequential forecasting method of Mechanical Engineering
Air quality prediction
5. Image Analysis based on Var-Lstm method forMathematical Statistician and 2094-0343 0.2
Air quality prediction Engineering Applications
6. Air pollution detection from sky images withInternational Journal of early 1308-5581 0.1
deep classifiers childhood special education
7 Air pollution detection and prediction usingYet to be published
moving average in Indian cities (Springer Elsevier)
22

VII. THESIS ORGANIZATION
Chapter 1: - INTRODUCTION: About various models for predict air pollution,
goals and objectives, more about problem statement and study on pollution
cities in India.
Chapter 2: LITERATURE REVIEW means discussion about different models
of predicting air pollution of different authors.
Chapter 3: CONCEPTS: Explanation about Existing system of Air pollution
monitoring system and detection system using sensors, used models and used
algorithms.
Chapter 4: PROPOSED SYSTEM: Design of the proposed system, algorithm
approach, architecture of Multivariate Sequential forecasting model and
design of predicting air pollution using IoT.
Chapter 5: IMPLEMENTATION: Before applying proposed model need to
preprocess and exploratory data analysis of air quality index (AQI) data and
time series dataset. Then implementation of proposed models and used
technologies explanation.
Chapter 6: RESULTS AND DISCUSSIONS: Explain about Results and
discussion of various models. And compare both the proposed model like air
pollution detection using IoT and air pollution predicting using Multivariate
Sequential forecasting model.
Chapter 7: CONCLUSION: Conclusion of study and future scopes of present
work.
*****
23

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