The quality of air is very important to living beings on the earth. In this research paper, a model is developed for the ealuation of air quality called Adaptive Neuro-Fuzzy Inference System (ANFIS). The four air pollution variables: SO2, NO2, RSPM, and TSPM of Chennai city from 2006 to 2008 are used in the development of the ANFIS model. The implied model is reciprocated alongside the Indian air quality index (IAQI), and it is ascertained such so the implicate model resulted with acknowledging the legitimate prevision of air quality.

1.
N.
Ali
,
Elshafie
Ahmed
,
AK
Othman
,
J.
Othman
,
Prediction of johon river water quality parameters using artificial neural networks.
Eur journal Scientific Res.
28
(
3
) ,
422
35
(
2009
).
2.
E.A.
Cox
,
Fuzzy Fundamentals
,
IEEE Spectrum
,
29
(
10
)
58
61
(
1992
).
3.
B.
Fisher
,
Fuzzy environmental decision-making: application to air pollution, Atmos Environ.
,
37
1865
1877
(
2003
).
4.
F.
Herrera
,
M.
Lozano
,
Fuzzy adaptive genetic algorithm: design, taxonomy, and future directions
.
Soft Computing.
7
,
545
562
(
2003
).
5.
J-SR
Jang
,
C-T
Sun
,
E.
Mizutan
, Neuro-fuzzy, and soft computing,(
Prentice Hall of India Private Limited
,
New Delhi
,
2005
), pp.
333
342
.
6.
J.
Thevaril
,
H. K
Kwan
,
Speech Enhancement using Adaptive Neuro-Fuzzy Filtering, Intelligent Signal Processing, and Communication System
. (
2005
)
753
755
.
7.
R.
Kumaravel
,
J. S.
Sudarsan
,
S. Vemu
Kumari
, Jyothirmai, and
Sija
Arun
,
Water quality index estimation using fuzzy optimization technique
,
AIP Conference Proceedings
2277
,
090013
(
2020
).
8.
R.
Kumaravel
,
J.S.
Sudarsan
,
D. Justus
Reymond
,
S.
Ramesh
,
B. Nithesh
Ikshwaak
,
Surface lake water quality index estimation using fuzzy logic interface
,
AIP Conference Proceedings
2277
,
090010
(
2020
).
9.
R.
Kumaravel
,
V.
Vallinayagam
,
A fuzzy inference system for pond water quality using MATLAB
.
Ultra-Scientist of Physical sciences.
24
(
1
) (
2012
)
85
94
.
10.
R.
Kumaravel
,
V.
Vallinayagam
,
P.
Venkatesan
,
A Fuzzy Neural System for Water Quality Prediction
,
International Review of Fuzzy Mathematics.
12
(
1
) (
2017
)
95
111
.
11.
R.
Kumaravel
,
V.
Vallinayagam
,
A Fuzzy Inference system for Air Quality in Chennai using MATLAB
,
Environmental Research and Development.
07
(
2012
)
484
95
.
12.
E. H.
Mamdani
,
S.
Assilian
,
An experiment in linguistic synthesis with a fuzzy logic Controller
.
Int J. Man-mach stud.
7
(
1
) (
1975
)
1
13
.
13.
D.W.
Ocampo
,
H. N.
Ferre
,
J.L.
Domingo
,
M.
Schuhmacher
,
Assessing water quality in rivers with fuzzy inference systems: A case study
.
Environ Int
32
(
2006
)
733
742
.
14.
M.
Sugeno
,G
T.
Kang
,
Structure identification of the fuzzy model
.
Fuzzy Sets Syst.
28
(
1
) (
1988
)
15
33
.
15.
T.
Takagi
,
M.
Sugeno
,
Fuzzy identification of systems and its application to modelling and control
.
IEEE Trans Syst Man Cybernet.
15
(
1
) (
1985
)
116
32
.
16.
Muhammad Muhitur
Rahman
,
Md
Shafiullah
,
Syed Masiur
Rahman
,
Abu Nasser
Khondaker
,
Abduljamiu
Amao
and
Md. Hasan
Zahir
,
Soft Computing Applications in Air Quality Modeling: Past, Present, and Future
,
Sustainability
2020
,
12
,
4045
.
17.
He
Zhang
,
Ravi
Srinivasan
and
Xu
Yang
,
Simulation and Analysis of Indoor Air Quality in Florida Using Time Series Regression (TSR) and Artificial Neural Networks (ANN) Models
,
Symmetry
2021
,
13
,
952
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