I
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S In
t
er
na
t
io
na
l J
o
urna
l o
f
Art
if
icia
l In
t
ellig
ence
(
I
J
-
AI
)
Vo
l.
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5
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515
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Air quality
predic
tion usin
g
boo
stin
g
-
ba
sed ma
chine l
ea
rning
mo
dels for sus
tai
na
ble enviro
nme
nt
Ahma
d
F
a
uz
i
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a
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mp
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n
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Art
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nfo
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ticle
his
to
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y:
R
ec
eiv
ed
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2
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2
0
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4
R
ev
is
ed
No
v
1
0
,
2
0
2
5
Acc
ep
ted
Dec
1
5
,
2
0
2
5
Hig
h
le
v
e
ls
o
f
a
ir
p
o
ll
u
ti
o
n
a
r
e
e
x
trem
e
ly
h
a
rm
fu
l
t
o
h
u
m
a
n
s
a
n
d
t
h
e
e
n
v
iro
n
m
e
n
t.
Th
e
y
i
n
c
re
a
se
th
e
risk
o
f
re
sp
irat
o
ry
in
fe
c
ti
o
n
s
a
n
d
lu
n
g
c
a
n
c
e
r,
e
sp
e
c
ially
a
m
o
n
g
v
u
l
n
e
ra
b
le
p
o
p
u
lati
o
n
s.
T
h
e
re
fo
re
,
d
e
v
e
lo
p
i
n
g
e
ffe
c
ti
v
e
p
o
ll
u
ti
o
n
c
o
n
tro
l
m
e
a
su
re
s
is
c
ru
c
ial
fo
r
m
it
ig
a
ti
n
g
th
e
se
n
e
g
a
ti
v
e
imp
a
c
ts.
We
n
e
e
d
to
imp
lem
e
n
t
e
ffe
c
ti
v
e
m
e
th
o
d
s
t
o
p
re
d
ict
a
n
d
m
a
n
a
g
e
a
ir
q
u
a
li
t
y
f
o
r
th
e
sa
k
e
o
f
p
u
b
li
c
h
e
a
lt
h
a
n
d
a
h
e
a
lt
h
ier
e
n
v
ir
o
n
m
e
n
t
.
In
re
c
e
n
t
y
e
a
rs,
m
a
c
h
in
e
lea
rn
in
g
(M
L)
m
e
th
o
d
s
h
a
v
e
b
e
e
n
i
n
c
re
a
sin
g
ly
u
ti
li
z
e
d
in
a
ir
q
u
a
li
t
y
p
re
d
icti
o
n
d
u
e
to
t
h
e
ir
a
b
il
it
y
t
o
a
n
a
ly
z
e
d
a
tas
e
ts
a
n
d
id
e
n
ti
f
y
c
o
m
p
lex
p
a
tt
e
rn
s.
H
o
we
v
e
r,
t
h
e
re
li
a
b
il
it
y
a
n
d
a
c
c
u
ra
c
y
o
f
a
ir
q
u
a
li
ty
p
re
d
ictio
n
m
o
d
e
ls
re
m
a
in
a
c
h
a
ll
e
n
g
e
.
Th
is
st
u
d
y
p
r
o
p
o
se
s
a
b
o
o
s
ti
n
g
-
b
a
se
d
M
L
m
o
d
e
l
f
o
r
p
re
d
ictin
g
a
ir
q
u
a
li
ty
.
We
imp
lem
e
n
ted
th
re
e
sta
g
e
s
in
th
e
p
ro
p
o
se
d
m
e
th
o
d
.
I
n
th
e
first
st
a
g
e
,
we
c
o
n
d
u
c
te
d
d
a
ta
p
re
p
r
o
c
e
ss
in
g
a
n
d
a
n
a
ly
sis
to
e
li
m
in
a
te
n
o
ise
,
re
m
o
v
e
re
d
u
n
d
a
n
t
d
a
ta,
a
n
d
e
n
c
o
d
e
c
a
teg
o
rica
l
fe
a
tu
re
s.
In
t
h
e
se
c
o
n
d
sta
g
e
,
we
p
re
d
icte
d
a
ir
q
u
a
li
ty
c
a
teg
o
ries
b
y
lev
e
ra
g
in
g
2
5
M
L
m
o
d
e
ls,
d
iv
id
i
n
g
t
h
e
m
in
to
t
h
re
e
d
isti
n
c
t
c
a
teg
o
ries
.
Th
e
re
su
lt
s
sh
o
w
t
h
a
t
th
e
e
x
trem
e
g
r
a
d
ien
t
b
o
o
st
in
g
(XG
Bo
o
st),
li
g
h
t
g
ra
d
ien
t
b
o
o
st
in
g
m
a
c
h
i
n
e
(LG
BM
),
a
n
d
a
d
a
p
ti
v
e
b
o
o
sti
n
g
(Ad
a
Bo
o
st
)
m
o
d
e
ls
o
u
t
p
e
rfo
rm
t
h
e
o
th
e
rs
i
n
a
ir
q
u
a
li
ty
p
re
d
icti
o
n
,
a
c
h
iev
i
n
g
a
n
a
c
c
u
ra
c
y
o
f
9
9
%
.
F
in
a
ll
y
,
we
c
o
m
p
a
re
d
t
h
e
se
th
re
e
m
o
d
e
ls
u
si
n
g
1
0
-
f
o
ld
c
ro
ss
-
v
a
li
d
a
ti
o
n
t
o
e
n
s
u
re
th
e
y
g
e
n
e
ra
li
z
e
we
ll
in
las
t
sta
g
e
.
K
ey
w
o
r
d
s
:
Ad
aBo
o
s
t c
lass
if
ier
Air
q
u
ality
p
r
e
d
ictio
n
L
GB
M
clas
s
if
ier
Ma
ch
in
e
lear
n
in
g
XGBo
o
s
t
clas
s
if
ier
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Ma
h
ar
in
a
I
n
f
o
r
m
atio
n
Sy
s
tem
s
Pro
g
r
am
,
Facu
lty
o
f
C
o
m
p
u
ter
Scien
ce
,
Un
iv
er
s
itas
B
u
an
a
Per
ju
an
g
a
n
Kar
awa
n
g
H.
S.
R
o
n
g
g
o
walu
y
o
Stre
et,
T
e
lu
k
J
am
b
e,
Kar
awa
n
g
,
I
n
d
o
n
e
s
ia
E
m
ail:
m
ah
ar
in
a@
u
b
p
k
ar
awa
n
g
.
ac
.
id
1.
I
NT
RO
D
UCT
I
O
N
A
i
r
p
o
l
l
u
ti
o
n
r
e
f
e
r
s
t
o
t
h
e
p
r
ese
n
c
e
o
f
h
a
r
m
f
u
l
s
u
b
s
t
a
n
c
es
i
n
th
e
a
i
r
t
h
a
t a
d
v
e
r
s
el
y
a
f
f
e
c
t h
e
a
lt
h
[
1
]
–
[
3
]
.
T
h
ese
h
ar
m
f
u
l
s
u
b
s
tan
ce
s
ca
n
b
e
f
in
e
p
ar
ticles,
to
x
ic
g
ases
,
o
r
o
th
er
ch
em
ical
co
m
p
o
u
n
d
s
s
u
s
p
en
d
ed
in
th
e
atm
o
s
p
h
er
e.
W
h
en
in
h
aled
,
th
ese
s
u
b
s
tan
ce
s
ca
n
ca
u
s
e
v
a
r
io
u
s
h
ea
lth
p
r
o
b
lem
s
,
s
u
ch
as
ir
r
itatio
n
o
f
t
h
e
e
y
es
an
d
th
r
o
at
t
o
c
h
r
o
n
ic
r
esp
ir
at
o
r
y
d
is
ea
s
es.
Ad
d
itio
n
ally
,
ai
r
p
o
llu
tio
n
c
an
h
av
e
wid
esp
r
ea
d
en
v
ir
o
n
m
en
tal
im
p
ac
ts
,
in
clu
d
in
g
d
am
a
g
e
to
p
lan
ts
,
an
im
als,
an
d
en
tire
ec
o
s
y
s
tem
s
[
4
]
,
[
5
]
.
Air
p
o
llu
tio
n
,
wh
ich
h
as
m
an
y
h
ar
m
f
u
l
ef
f
ec
ts
[
6
]
,
[
7
]
m
u
s
t
b
e
av
o
id
ed
,
an
d
th
er
ef
o
r
e
e
f
f
ec
tiv
e
m
an
ag
em
en
t
m
ea
s
u
r
e
s
ar
e
n
ec
ess
ar
y
.
I
n
s
u
p
p
o
r
tin
g
s
u
s
tain
ab
le
u
r
b
an
d
ev
elo
p
m
e
n
t,
ac
cu
r
ate
air
q
u
a
lity
m
o
n
ito
r
in
g
an
d
p
r
ed
ictio
n
tech
n
o
lo
g
ies
p
lay
a
cr
u
cial
r
o
le
[
8
]
,
[
9
]
.
T
h
ese
t
ec
h
n
o
lo
g
ies
p
r
o
v
id
e
ess
en
tial
g
u
id
a
n
ce
f
o
r
d
ec
is
io
n
-
m
ak
in
g
r
elate
d
to
u
r
b
an
en
v
ir
o
n
m
en
tal
m
an
a
g
em
en
t
[
1
0
]
,
[
1
1
]
.
Sev
er
al
s
tu
d
ies
o
n
air
q
u
ality
p
r
ed
ictio
n
u
s
in
g
m
ac
h
in
e
lear
n
in
g
(
ML
)
h
av
e
b
ee
n
c
o
n
d
u
cted
u
s
in
g
v
ar
io
u
s
m
eth
o
d
s
ac
r
o
s
s
d
if
f
er
en
t
lo
ca
tio
n
s
[
1
2
]
–
[
2
0
]
.
R
esear
ch
b
y
I
m
am
et
a
l.
[
2
1
]
in
R
a
b
in
d
r
a
an
d
Victo
r
ia,
I
n
d
ia,
em
p
lo
y
ed
s
u
p
p
o
r
t
v
ec
t
o
r
class
if
ier
(
SVC
)
an
d
r
an
d
o
m
f
o
r
est
(
R
F)
tech
n
iq
u
es.
T
h
is
s
tu
d
y
f
o
cu
s
ed
o
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
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I
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tif
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tell
,
Vo
l.
1
5
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
6
:
515
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5
2
3
516
p
o
llu
tan
ts
s
u
ch
as
PM
2.
5
,
PM
10
,
NO
2
,
C
O,
SO
2
,
an
d
O
3
.
Du
r
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ata
p
r
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s
in
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s
tag
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ata
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allo
ca
ted
f
o
r
tr
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d
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f
o
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test
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g
.
T
h
e
ex
p
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al
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esu
lts
r
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ted
ac
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ab
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Victo
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ia,
r
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s
p
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tiv
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.
Me
an
wh
ile,
th
e
r
esear
ch
in
[
1
5
]
,
[
2
2
]
u
s
ed
an
8
0
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0
s
p
lit
r
atio
f
o
r
th
eir
tr
ain
in
g
an
d
test
d
atasets
.
Kh
ad
o
m
et
a
l
.
[
2
2
]
p
r
o
p
o
s
ed
th
e
u
s
e
o
f
m
u
ltil
ay
er
p
er
ce
p
tr
o
n
(
ML
P)
an
d
lo
n
g
s
h
o
r
t
-
ter
m
m
e
m
o
r
y
(
L
STM
)
m
o
d
els
to
p
r
ed
ict
air
q
u
ality
i
n
B
ag
h
d
ad
,
wh
ile
J
an
ar
t
h
an
an
et
a
l.
[
1
5
]
em
p
lo
y
e
d
L
STM
to
f
o
r
ec
ast air
q
u
ality
in
I
n
d
ia.
R
ec
en
t
s
tu
d
y
in
air
q
u
ality
p
r
e
d
ictio
n
was
co
n
d
u
cte
d
b
y
R
esti
et
a
l
.
[
2
3
]
i
n
Sh
an
g
h
ai,
C
h
in
a,
u
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d
e
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s
em
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le
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aïv
e
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ay
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NB
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d
ec
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io
n
tr
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DT
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an
d
R
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m
eth
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s
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ce
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h
ig
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ac
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(
9
9
.
8
9
%).
Similar
ly
,
L
iv
in
g
s
t
o
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et
a
l.
[
2
4
]
s
tu
d
y
in
B
eijin
g
also
m
an
u
ally
s
p
lit
th
e
d
ataset
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h
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tu
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ap
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tech
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ar
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s
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h
u
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ity
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wev
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o
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im
p
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k
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f
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tech
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h
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ab
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1
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T
h
e
u
s
e
o
f
m
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ataset
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f
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h
t
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e
p
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g
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e
lack
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m
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r
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en
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ay
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el's ab
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T
ab
le
1
.
Pre
v
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o
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s
r
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air
q
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ality
p
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Ta
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l
.
[
2
4
]
B
e
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F
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y
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M
a
n
u
a
l
l
y
No
K
h
a
d
o
m
e
t
a
l
.
[
2
2
]
B
a
g
h
d
a
d
,
I
r
a
q
M
LP
a
n
d
LSTM
PM
2
.
5
R
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:
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a
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a
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h
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n
a
n
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t
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l
.
[
1
5
]
I
n
d
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a
LSTM
C
O
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S
O
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2
,
P
M
2
.
5
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ssi
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0
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No
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mam
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t
a
l
.
[
2
1
]
V
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c
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,
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d
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RF
PM
2.
5
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M
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3
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l
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mam
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l
.
[
2
1
]
R
a
b
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d
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,
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PM
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R
e
st
i
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t
a
l
.
[
2
3
]
S
h
a
n
g
h
a
i
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C
h
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a
En
se
mb
l
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B
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e
a
t
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d
a
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m
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p
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v
a
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a
b
l
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m
p
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s
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o
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r
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l
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M
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No
T
o
ad
d
r
ess
th
e
is
s
u
e,
th
is
s
tu
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p
r
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p
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a
b
o
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g
-
b
ase
d
ML
m
o
d
el
f
o
r
p
r
ed
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a
ir
q
u
ality
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o
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g
alg
o
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ith
m
s
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g
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t)
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ex
tr
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r
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t
b
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h
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g
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g
m
ac
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e
(
L
GB
M)
,
h
av
e
s
h
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wn
p
r
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m
is
in
g
r
esu
lts
in
v
ar
io
u
s
p
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d
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n
task
s
d
u
e
to
th
eir
ab
ilit
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to
h
an
d
le
co
m
p
lex
d
at
a
an
d
th
eir
r
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tn
ess
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s
t
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itti
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g
.
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h
e
m
ain
co
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tr
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th
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p
ap
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lies
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f
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ly
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s
p
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b
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also
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s
its
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ig
h
ac
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r
ac
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cr
o
s
s
-
v
alid
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tech
n
i
q
u
es a
r
e
ap
p
lied
.
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h
e
k
ey
f
i
n
d
in
g
s
ca
n
b
e
s
u
m
m
ar
ized
as:
‒
W
e
p
r
o
p
o
s
e
u
s
in
g
b
o
o
s
tin
g
-
b
ased
ML
m
o
d
els
(
XGBo
o
s
t,
L
GB
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an
d
Ad
aBo
o
s
t
)
to
ac
c
u
r
ately
p
r
e
d
ict
air
q
u
ality
.
‒
Am
o
n
g
2
5
ML
m
o
d
els,
p
er
f
o
r
m
an
ce
o
f
XGBo
o
s
t
o
u
t
p
er
f
o
r
m
s
th
e
o
th
er
s
.
‒
T
h
is
m
eth
o
d
was d
em
o
n
s
tr
ated
u
s
in
g
a
f
l
o
o
d
d
ataset
f
r
o
m
J
a
k
ar
ta,
I
n
d
o
n
esia.
‒
W
e
u
tili
ze
th
e
p
r
o
p
o
s
ed
b
o
o
s
tin
g
m
eth
o
d
s
(
XGBo
o
s
t,
L
GB
M,
an
d
Ad
aBo
o
s
t
)
with
1
0
-
f
o
l
d
c
r
o
s
s
-
v
alid
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n
to
m
i
n
im
ize
b
ias an
d
en
s
u
r
e
th
at
t
h
e
m
o
d
el'
s
p
er
f
o
r
m
an
ce
is
g
en
er
alize
d
.
2.
M
AT
E
R
I
AL
A
ND
M
E
T
H
O
D
T
h
e
m
eth
o
d
o
lo
g
y
o
f
th
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s
tu
d
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co
n
s
is
ts
o
f
s
ev
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al
k
ey
s
tep
s
as
s
h
o
wn
in
Fig
u
r
e
1
.
T
h
e
i
n
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s
tag
e
in
v
o
lv
es d
ata
p
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p
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s
s
in
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aly
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,
wh
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in
clu
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es h
a
n
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lin
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m
is
s
in
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v
alu
es,
ad
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ed
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d
an
t d
ata,
en
co
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r
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d
s
p
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th
e
d
ataset
in
to
tr
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in
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test
in
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s
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.
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p
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s
s
is
cr
u
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f
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en
s
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in
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th
at
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y
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ltima
tely
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eq
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en
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t
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ataset
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air
q
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8
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3
8
A
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ti
lized
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1
6
]
.
T
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ataset
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s
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lit in
to
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% f
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o
n
e
o
f
t
h
e
p
a
r
a
m
e
t
e
r
s me
a
s
u
r
e
d
I
n
t
e
g
e
r
7
NO
2
N
i
t
r
o
g
e
n
d
i
o
x
i
d
e
i
s
o
n
e
o
f
t
h
e
p
a
r
a
me
t
e
r
s m
e
a
s
u
r
e
d
I
n
t
e
g
e
r
8
M
a
x
Th
e
h
i
g
h
e
st
mea
s
u
r
e
d
v
a
l
u
e
o
f
a
l
l
p
a
r
a
met
e
r
s m
e
a
s
u
r
e
d
a
t
t
h
e
sa
me
t
i
m
e
I
n
t
e
g
e
r
9
C
r
i
t
i
c
a
l
Th
e
p
a
r
a
m
e
t
e
r
w
i
t
h
t
h
e
h
i
g
h
e
s
t
me
a
s
u
r
e
men
t
r
e
s
u
l
t
s
S
t
r
i
n
g
10
C
a
t
e
g
o
r
y
C
a
t
e
g
o
r
y
o
f
r
e
s
u
l
t
s
o
f
a
i
r
p
o
l
l
u
t
i
o
n
s
t
a
n
d
a
r
d
i
n
d
e
x
c
a
l
c
u
l
a
t
i
o
n
S
t
r
i
n
g
11
Lo
c
a
t
i
o
n
s
M
e
a
su
r
e
me
n
t
l
o
c
a
t
i
o
n
a
t
t
h
e
s
t
a
t
i
o
n
S
t
r
i
n
g
T
ab
le
3
.
T
h
e
d
etailed
o
f
d
atas
et
No
D
a
t
e
PM
10
PM
2
.
5
SO
2
CO
O
3
NO
2
M
a
x
C
r
i
t
i
c
a
l
C
a
t
e
g
o
r
y
Lo
c
a
t
i
o
n
1
1
0
/
6
/
2
0
2
1
66
1
0
3
.
0
66
10
58
35
1
0
3
PM
2.
5
U
n
h
e
a
l
t
h
y
D
K
I
4
2
3
/
5
/
2
0
2
1
65
8
1
.
0
54
16
59
30
81
PM
2.
5
M
o
d
e
r
a
t
e
D
K
I
2
3
6
/
2
4
/
2
0
2
1
80
1
1
9
.
0
54
19
42
52
1
1
9
PM
2.
5
U
n
h
e
a
l
t
h
y
D
K
I
4
4
9
/
2
1
/
2
0
2
1
61
9
9
.
0
52
11
58
36
99
PM
2.
5
M
o
d
e
r
a
t
e
D
K
I
4
5
1
0
/
2
1
/
2
0
2
1
53
7
4
.
0
61
11
57
32
74
PM
2.
5
M
o
d
e
r
a
t
e
D
K
I
3
6
1
2
/
4
/
2
0
2
1
50
6
5
.
0
45
13
43
16
65
PM
2.
5
M
o
d
e
r
a
t
e
D
K
I
3
7
1
1
/
2
7
/
2
0
2
1
37
5
6
.
0
41
10
45
22
56
PM
2.
5
M
o
d
e
r
a
t
e
D
K
I
4
8
1
2
/
2
/
2
0
2
1
35
5
6
.
0
42
7
40
14
56
PM
2.
5
M
o
d
e
r
a
t
e
D
K
I
4
9
9
/
1
8
/
2
0
2
1
57
1
0
1
.
0
53
9
51
22
1
0
1
PM
2.
5
U
n
h
e
a
l
t
h
y
D
K
I
4
…
…
…
…
…
…
…
…
…
…
…
…
3
4
6
1
2
/
1
3
/
2
0
2
1
53
6
8
.
0
44
11
34
23
68
PM
2.
5
M
o
d
e
r
a
t
e
D
K
I
3
2
.
1
.
Da
t
a
pre
-
pro
ce
s
s
ing
Pre
p
r
o
ce
s
s
in
g
m
eth
o
d
s
p
lay
a
cr
u
cial
r
o
le
in
d
ev
elo
p
in
g
ac
cu
r
ate
ML
m
o
d
els
[
2
1
]
,
[
2
5
]
.
T
h
e
in
itial
s
tag
e
o
f
th
e
an
aly
s
is
in
v
o
lv
es
d
ata
p
r
ep
r
o
ce
s
s
in
g
,
wh
ich
is
in
clu
d
es
h
an
d
lin
g
m
is
s
in
g
v
a
lu
es
an
d
r
em
o
v
in
g
r
ed
u
n
d
an
t
d
ata.
I
n
a
d
d
r
ess
in
g
m
is
s
in
g
v
alu
es,
r
o
ws
co
n
tain
in
g
n
o
t
a
n
u
m
b
er
(
NaN
)
v
alu
es
in
th
e
PM
2.
5
v
ar
iab
le
ar
e
r
em
o
v
ed
to
en
s
u
r
e
th
e
in
teg
r
ity
o
f
th
e
d
ataset.
Su
ch
m
is
s
in
g
v
alu
es
ca
n
lead
to
m
is
in
ter
p
r
etatio
n
an
d
in
ac
cu
r
ate
p
r
ed
ictio
n
s
,
as
th
e
m
o
d
el
lack
s
th
e
co
m
p
lete
in
f
o
r
m
atio
n
n
ec
ess
ar
y
f
o
r
m
a
k
in
g
p
r
e
d
ictio
n
.
A
v
is
u
aliza
tio
n
o
f
th
e
m
is
s
in
g
v
alu
es
in
th
e
PM
2.
5
f
ea
tu
r
e
is
p
r
esen
ted
in
Fig
u
r
e
2
.
Fu
r
t
h
er
m
o
r
e,
r
e
d
u
n
d
an
t
d
ata,
ar
e
elim
in
ated
to
p
r
ev
en
t
b
ias in
th
e
an
aly
s
is
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
5
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
6
:
515
-
5
2
3
518
Fig
u
r
e
2
.
Gr
a
p
h
o
f
th
e
tim
e
s
e
r
ies f
o
r
th
e
PM
2.
5
f
ea
t
u
r
e
s
h
o
w
in
g
m
is
s
in
g
v
alu
es
T
h
e
p
r
esen
ted
co
r
r
elatio
n
m
at
r
ix
as
s
h
o
wn
in
Fig
u
r
e
3
o
f
f
er
s
an
an
aly
s
is
o
f
th
e
r
elatio
n
s
h
ip
s
am
o
n
g
v
ar
io
u
s
air
q
u
ality
p
ar
am
eter
s
with
in
th
is
s
tu
d
y
.
T
h
e
h
ea
tm
ap
d
em
o
n
s
tr
ate
th
e
co
r
r
ela
tio
n
o
f
air
q
u
ality
f
ea
tu
r
es,
with
y
ello
w
in
d
icatin
g
a
p
o
s
itiv
e
co
r
r
elatio
n
a
n
d
d
ar
k
b
lu
e
r
e
p
r
esen
tin
g
a
n
e
g
ativ
e
co
r
r
elatio
n
.
L
ig
h
ter
s
h
ad
es in
d
icate
a
s
tr
o
n
g
er
co
r
r
elatio
n
b
etwe
en
t
h
e
f
e
atu
r
es.
Fig
u
r
e
3
.
C
o
r
r
elatio
n
h
ea
tm
a
p
o
f
th
e
air
q
u
ality
d
ataset
2
.
2
.
P
re
dict
io
n
I
n
s
ec
o
n
d
s
tag
e,
we
u
tili
ze
s
ev
er
al
ML
in
clu
d
in
g
Ad
aB
o
o
s
t
[
1
4
]
,
XGBo
o
s
t
[
2
6
]
,
L
GB
M
[
1
7
]
,
R
F
[
2
6
]
,
DT
[
2
6
]
,
e
x
tr
a
tr
ee
s
,
b
ag
g
in
g
,
s
to
ch
asti
c
g
r
ad
ien
t d
escen
t
(
SGD)
c
lass
if
ier
,
lo
g
is
tic
r
eg
r
ess
io
n
,
lin
ea
r
d
is
cr
im
in
an
t
an
aly
s
is
,
n
ea
r
est
ce
n
tr
o
id
,
lab
el
p
r
o
p
ag
atio
n
,
lab
el
s
p
r
ea
d
in
g
,
p
er
ce
p
t
r
o
n
,
lin
ea
r
SVC
[
2
6
]
,
p
ass
iv
e
ag
g
r
ess
iv
e
class
if
ier
,
ca
lib
r
ated
class
if
ier
cr
o
s
s
-
v
alid
atio
n
(
CV
)
,
r
id
g
e
class
if
ier
[
2
6
]
,
r
i
d
g
e
class
if
ier
C
V
[
2
6
]
,
SVC
[
2
6
]
,
B
er
n
o
u
ll
i
n
aïv
e
B
ay
es
(
B
er
n
o
u
lliNB
)
,
q
u
ad
r
atic
d
is
cr
im
in
an
t
an
al
y
s
is
,
G
au
s
s
ian
n
aïv
e
B
ay
es
(
Gau
s
s
ian
NB
)
,
k
-
n
ea
r
e
s
t
n
eig
h
b
o
r
s
(
KNN
)
[
2
6
]
,
an
d
d
u
m
m
y
class
if
ier
.
I
n
t
h
ir
d
s
tag
e
,
we
lev
er
ag
e
th
r
ee
m
o
d
els:
XGBo
o
s
t,
L
ig
h
tGB
M,
an
d
Ad
aBo
o
s
t.
T
h
es
e
m
o
d
els
ar
e
s
elec
ted
to
ev
alu
ate
an
d
co
m
p
ar
e
class
if
icatio
n
p
er
f
o
r
m
an
ce
ac
r
o
s
s
d
if
f
er
en
t le
ar
n
i
n
g
ap
p
r
o
ac
h
es.
2
.
3
.
Cro
s
s
-
v
a
lid
a
t
io
n t
ec
hn
iqu
e
I
n
th
e
th
ir
d
s
tag
e,
we
im
p
lem
en
ted
1
0
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
.
T
h
is
tech
n
iq
u
e
d
iv
id
es
th
e
d
ataset
in
to
1
0
s
u
b
s
ets
(
f
o
ld
s
)
,
wh
er
e
ea
ch
s
u
b
s
et
tak
es
t
u
r
n
s
s
er
v
i
n
g
as
t
h
e
test
s
et
wh
ile
t
h
e
r
em
ain
in
g
9
s
u
b
s
ets
ar
e
u
s
ed
as
th
e
tr
ain
in
g
s
et.
T
h
is
p
r
o
ce
s
s
i
s
r
ep
ea
ted
1
0
tim
es,
en
s
u
r
in
g
th
at
ea
ch
s
u
b
s
et
ac
ts
as
th
e
test
s
et
ex
ac
tly
o
n
ce
.
T
h
e
p
r
im
a
r
y
g
o
al
o
f
th
i
s
m
eth
o
d
is
to
r
e
d
u
ce
b
ias
in
t
h
e
m
o
d
el
an
d
im
p
r
o
v
e
th
e
m
o
d
el'
s
g
en
er
aliza
tio
n
to
n
ew
d
ata.
2
.
4
.
E
v
a
lua
t
i
o
n m
o
del
I
n
o
r
d
er
to
ev
alu
ate
th
e
p
r
o
p
o
s
ed
m
eth
o
d
f
o
r
wate
r
q
u
alit
y
p
r
ed
ictio
n
,
we
u
tili
ze
d
ac
c
u
r
ac
y
an
d
F1
-
s
co
r
e
m
etr
ics,
as
p
r
esen
te
d
in
T
ab
le
4
.
T
P
r
ef
er
to
tr
u
e
p
o
s
itiv
e
,
FP
f
o
r
f
alse
p
o
s
itiv
e
,
FN
f
o
r
f
alse
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
ir
q
u
a
lity p
r
ed
ictio
n
u
s
in
g
b
o
o
s
tin
g
-
b
a
s
ed
ma
ch
in
e
le
a
r
n
in
g
mo
d
els fo
r
s
u
s
ta
in
a
b
le
…
(
A
h
ma
d
F
a
u
z
i
)
519
n
eg
ati
ve
,
an
d
T
N
f
o
r
tr
u
e
n
eg
ativ
e
.
T
h
ese
m
etr
ics
p
r
o
v
id
e
a
co
m
p
r
e
h
en
s
iv
e
ass
ess
m
en
t
o
f
th
e
m
o
d
el'
s
p
er
f
o
r
m
an
ce
,
allo
win
g
u
s
to
d
eter
m
in
e
its
ef
f
ec
tiv
en
ess
in
c
o
r
r
ec
tly
p
r
ed
ictin
g
air
q
u
ality
.
T
ab
le
4
.
E
v
alu
atio
n
m
o
d
el
eq
u
atio
n
s
M
e
t
r
i
c
s
Eq
u
a
t
i
o
n
s
A
c
c
u
r
a
c
y
(
TP+TN
)
/
(
TP+TN
+
F
P
+
F
N
)
F1
-
sc
o
r
e
2
TP/(
2
TP+F
P
+
F
N
)
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
ab
le
5
h
i
g
h
lig
h
ts
th
e
p
er
f
o
r
m
an
ce
o
f
v
ar
io
u
s
ML
alg
o
r
ith
m
s
f
o
r
air
q
u
ality
p
r
ed
ictio
n
,
f
o
cu
s
in
g
o
n
th
eir
ac
cu
r
ac
y
,
F1
-
s
co
r
e,
an
d
in
f
er
en
ce
tim
e.
Ad
aBo
o
s
t,
XG
B
o
o
s
t,
L
GB
M,
R
F,
an
d
DT
ac
h
iev
ed
th
e
h
i
g
h
est
p
er
f
o
r
m
an
ce
with
ac
cu
r
ac
ies an
d
F1
-
s
co
r
es o
f
0
.
9
9
an
d
0
.
9
8
,
r
esp
ec
tiv
ely
.
No
tab
ly
,
Ad
aBo
o
s
t,
XGBo
o
s
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RE
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1
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