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tes
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t,
a
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su
sta
in
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le citi
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s.
K
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w
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s
:
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lear
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g
L
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g
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CC B
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C
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r
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p
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A
uth
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:
Kav
ita
J
h
ajh
ar
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Dep
ar
tm
en
t o
f
I
n
f
o
r
m
atio
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T
e
ch
n
o
lo
g
y
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Facu
lty
o
f
Scien
ce
,
T
ec
h
n
o
lo
g
y
,
a
n
d
Ar
c
h
itectu
r
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Ma
n
ip
al
Un
iv
er
s
ity
J
aip
u
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Deh
m
i K
alan
,
J
aip
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r
,
R
ajasth
an
,
I
n
d
ia
E
m
ail:
k
av
ita.
jh
ajh
ar
ia@
jaip
u
r
.
m
an
ip
al.
ed
u
1.
I
NT
RO
D
UCT
I
O
N
A
n
atu
r
al
p
h
en
o
m
en
o
n
ch
a
r
ac
ter
ized
b
y
h
ig
h
er
tem
p
er
atu
r
e
s
in
u
r
b
a
n
ar
ea
s
a
n
d
l
o
wer
o
n
es
in
r
u
r
al
s
u
r
r
o
u
n
d
in
g
ar
ea
s
is
th
e
u
r
b
a
n
h
ea
t
is
lan
d
(
UHI
)
ef
f
ec
t
ca
u
s
ed
b
y
h
u
m
an
a
n
d
u
r
b
an
in
f
r
a
s
tr
u
ctu
r
e
ac
tiv
ities
.
Pro
ce
ed
in
g
f
ac
to
r
s
lik
e
ex
ten
s
iv
e
u
s
e
o
f
h
ea
t
r
etain
in
g
m
ater
ials
lik
e
a
s
p
h
alt
an
d
co
n
cr
ete,
r
ed
u
ctio
n
in
v
eg
etativ
e
co
v
er
a
n
d
a
n
th
r
o
p
o
g
en
ic
h
ea
t
g
en
er
ate
d
b
y
v
e
h
icles
an
d
in
d
u
s
tr
ies
[
1
]
h
av
e
ca
u
s
ed
th
is
tem
p
er
at
u
r
e
d
is
p
ar
ity
.
Ur
b
an
izatio
n
wo
r
s
e
n
s
UHI
,
in
cr
ea
s
es c
o
o
lin
g
d
e
m
an
d
,
p
o
llu
tio
n
,
a
n
d
em
is
s
io
n
s
[
2
]
.
UHI
also
r
aises
h
ea
lth
r
is
k
s
[
3
]
,
h
ar
m
s
b
i
o
d
iv
e
r
s
ity
an
d
s
tr
ain
s
in
f
r
astru
ctu
r
e
[
4
]
.
Giv
e
n
s
u
ch
im
p
ac
ts
,
UHI
m
itig
atio
n
(
e.
g
co
o
l
r
o
o
f
s
a
n
d
g
r
ee
n
s
p
ac
es)
is
a
p
r
io
r
ity
[
5
]
,
s
u
p
p
o
r
ted
b
y
b
ette
r
u
n
d
er
s
tan
d
in
g
o
f
UHI
d
y
n
a
m
ics.
T
h
e
r
esear
c
h
q
u
esti
o
n
in
th
is
s
tu
d
y
is
as
f
o
l
lo
ws:
do
th
e
lan
d
s
ca
p
e
m
etr
ic
s
(
am
p
lifie
d
b
y
ad
v
a
n
ce
d
m
ac
h
in
e
lear
n
i
n
g
(
ML
)
an
d
d
ee
p
lear
n
in
g
(
DL
)
m
o
d
el
s
)
h
av
e
th
e
ca
p
ac
ity
to
p
r
o
p
er
ly
p
r
o
p
h
esy
th
e
in
ten
s
ity
o
f
U
HI
d
u
r
in
g
b
o
th
th
e
d
ay
an
d
n
ig
h
t scen
ar
io
s
in
v
a
r
io
u
s
u
r
b
a
n
r
eg
i
o
n
s
ar
o
u
n
d
t
h
e
g
lo
b
e?
UHI
im
p
ac
ts
s
p
an
en
v
ir
o
n
m
en
t,
p
u
b
lic
h
ea
lth
,
an
d
in
f
r
astru
ctu
r
e,
m
ak
in
g
th
em
ce
n
tr
a
l
to
u
r
b
an
p
lan
n
in
g
an
d
clim
ate
r
ea
d
i
n
ess
.
E
n
v
ir
o
n
m
en
tally
,
UHI
r
aises
co
o
lin
g
d
em
an
d
,
in
cr
ea
s
in
g
em
is
s
io
n
s
,
an
d
air
p
o
llu
tio
n
[
2
]
.
T
h
is
f
ee
d
b
ac
k
l
o
o
p
wo
r
s
en
s
UHI
an
d
ac
ce
ler
ate
s
clim
ate
ch
an
g
e.
UHI
r
aises
h
ea
t
-
r
elate
d
illn
ess
es
an
d
d
ea
th
s
,
esp
ec
ially
in
v
u
l
n
e
r
ab
le
g
r
o
u
p
s
[
3
]
.
Hig
h
er
tem
p
er
atu
r
es
also
in
cr
ea
s
e
v
ec
to
r
-
b
o
r
n
e
d
is
ea
s
e
s
p
r
ea
d
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
I
n
teg
r
a
tin
g
ma
c
h
in
e
lea
r
n
in
g
a
n
d
d
e
ep
lea
r
n
in
g
w
ith
la
n
d
s
c
a
p
e
metrics
fo
r
u
r
b
a
n
h
ea
t
(
S
i
d
d
h
a
r
th
P
a
l)
4829
[
6
]
.
UHI
ca
u
s
es
th
e
r
m
al
s
tr
ess
o
n
in
f
r
astru
ctu
r
e
,
r
aisi
n
g
m
ain
ten
an
ce
co
s
ts
[
7
]
.
UHI
also
t
h
r
ea
ten
s
b
io
d
iv
e
r
s
ity
b
y
f
o
r
cin
g
s
p
ec
ies
to
a
d
ap
t
t
o
h
ig
h
e
r
tem
p
er
at
u
r
es,
lead
in
g
to
ec
o
lo
g
ical
im
b
alan
ce
[
8
]
.
Ad
d
r
ess
in
g
th
ese
im
p
ac
ts
is
k
ey
f
o
r
r
esil
ien
t,
en
er
g
y
-
ef
f
icien
t,
s
u
s
tain
ab
le
cities.
Fu
n
d
am
en
tally
,
AI
is
c
h
an
g
in
g
h
o
w
we
s
tu
d
y
an
d
m
itig
ate
UHI
,
p
la
n
cities,
an
d
ad
a
p
t
t
o
clim
ate
ch
an
g
e.
ML
m
o
d
els
lik
e
co
n
v
o
lu
tio
n
al
n
e
u
r
al
n
etwo
r
k
s
(
C
NNs
)
,
r
an
d
o
m
f
o
r
est
(
RF
)
,
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM
)
an
aly
ze
s
atelli
te
an
d
s
en
s
o
r
d
ata
to
p
in
p
o
in
t
h
ig
h
-
r
is
k
ar
ea
s
an
d
ass
ess
tem
p
er
atu
r
e
f
lu
ctu
atio
n
s
[
9
]
,
[
1
0
]
.
Usi
n
g
lan
d
s
u
r
f
ac
e
tem
p
er
atu
r
e
(
L
ST)
a
n
d
lo
ca
l
clim
ate
zo
n
e
s
(
L
C
Z
s
)
d
ata
,
AI
h
elp
s
ev
al
u
ate
m
ater
ials
,
d
en
s
ity
,
a
n
d
v
eg
eta
tio
n
,
g
u
id
in
g
g
r
ee
n
in
f
r
astru
c
tu
r
e
d
esig
n
[
1
1
]
.
On
e
ex
am
p
le
is
h
o
w
ap
p
l
y
in
g
g
en
etic
alg
o
r
ith
m
s
an
d
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
allo
ws
u
r
b
an
p
la
n
n
er
s
to
esti
m
ate
h
o
w
well
d
if
f
er
e
n
t
in
ter
v
en
tio
n
s
,
i.e
.
,
th
e
e
x
p
an
s
i
o
n
o
f
g
r
ee
n
s
p
ac
es,
g
r
o
win
g
v
eg
etatio
n
,
o
r
u
s
e
o
f
r
ef
lectiv
e
m
ater
ial,
w
o
r
k
in
d
en
s
e
ar
ea
s
.
T
h
ey
i
d
en
tify
lan
d
u
s
e
p
lan
s
to
r
ed
u
ce
h
ea
t
a
n
d
b
o
o
s
t
r
esil
ien
ce
[
1
2
]
,
[
1
3
]
.
I
n
teg
r
atin
g
r
em
o
te
s
en
s
in
g
an
d
AI
en
ab
les
tailo
r
ed
,
d
ata
-
d
r
iv
e
n
UHI
m
itig
atio
n
,
s
u
p
p
o
r
tin
g
s
u
s
tain
ab
le
u
r
b
a
n
p
lan
n
in
g
an
d
p
u
b
lic
h
ea
lth
.
T
h
e
im
p
o
r
ta
n
ce
lies
in
co
m
b
in
in
g
ML
/DL
m
o
d
els
(
i
n
clu
d
in
g
lo
n
g
s
h
o
r
t
-
ter
m
m
e
m
o
r
y
(
L
STM
)
with
atten
tio
n
)
with
lar
g
e
-
s
ca
le
g
l
o
b
al
d
ata
(
NASA
So
cio
ec
o
n
o
m
ic
Data
an
d
Ap
p
licatio
n
s
C
en
ter
(
SEDA
C
)
)
.
I
n
clu
d
in
g
lan
d
s
ca
p
e
m
etr
ics
lik
e
u
r
b
an
s
ize,
p
o
p
u
latio
n
,
a
n
d
lo
ca
tio
n
r
ev
ea
ls
n
ew
s
p
atial
an
d
tem
p
o
r
al
UHI
tr
en
d
s
.
T
h
is
wo
r
k
is
u
n
i
q
u
e
s
i
n
ce
it
is
f
o
cu
s
ed
o
n
a
g
lo
b
al
s
ca
le,
b
ec
au
s
e
o
f
co
m
p
ar
in
g
th
e
ML
/DL
m
o
d
els
to
p
r
ed
ict
th
e
d
ay
tim
e
an
d
n
ig
h
ttime
UHI
,
an
d
p
r
o
v
id
in
g
r
e
co
m
m
en
d
atio
n
s
wh
ich
ca
n
b
e
u
tili
ze
d
b
y
u
r
b
an
p
lan
n
er
s
to
r
estrict
th
e
h
ea
t isl
an
d
ef
f
ec
ts
.
T
h
e
i
d
e
n
t
i
f
ic
a
t
i
o
n
o
f
U
H
I
i
s
n
o
w
t
r
a
n
s
f
o
r
m
a
t
i
v
e
i
n
a
d
d
r
e
s
s
i
n
g
t
h
e
i
n
c
r
e
as
i
n
g
h
e
at
is
s
u
es
u
n
d
e
r
t
h
e
u
r
b
a
n
l
a
n
d
s
c
a
p
e
c
o
n
s
i
d
e
r
i
n
g
t
h
e
A
I
in
n
o
v
a
t
i
o
n
i
n
as
s
ess
m
e
n
t
a
n
d
d
i
s
c
e
r
n
m
e
n
t
.
T
h
e
s
e
t
o
o
ls
u
s
e
s
at
e
l
l
it
e
,
c
l
i
m
at
e
,
a
n
d
s
p
a
t
i
al
d
a
t
a
t
o
t
r
a
c
k
U
H
I
p
r
e
cis
e
l
y
.
C
NN
s
a
n
al
y
z
e
s
a
t
el
l
it
e
im
a
g
e
s
t
o
m
a
p
t
e
m
p
e
r
a
t
u
r
e
g
r
ad
i
e
n
t
s
a
n
d
p
r
o
d
u
c
e
u
r
b
a
n
h
e
a
t
m
a
p
s
[
1
4
]
.
A
d
v
a
n
c
e
d
d
e
e
p
n
e
u
r
a
l
n
e
t
w
o
r
k
s
(
D
NN
s
)
a
n
d
r
e
c
u
r
r
e
n
t
n
e
u
r
a
l
n
e
t
wo
r
k
s
(
R
N
Ns
)
p
r
o
c
e
s
s
t
e
m
p
o
r
a
l
d
a
t
a
t
o
p
r
e
d
i
c
t
t
e
m
p
e
r
a
t
u
r
e
t
r
e
n
d
s
,
h
e
l
p
i
n
g
p
l
a
n
n
e
r
s
r
e
d
u
c
e
h
e
a
t
e
f
f
e
c
t
s
[
1
5
]
.
G
e
o
g
r
a
p
h
i
c
i
n
f
o
r
m
a
t
i
o
n
s
y
s
te
m
(
G
I
S
)
w
i
t
h
A
I
c
r
e
a
te
s
la
y
e
r
e
d
h
e
a
t
m
a
p
s
s
h
o
w
i
n
g
w
h
er
e
c
o
o
l
i
n
g
m
e
a
s
u
r
es
li
k
e
v
e
g
e
ta
t
i
o
n
w
o
u
l
d
b
e
m
o
s
t
e
f
f
e
c
t
i
v
e
[
1
6
]
.
M
L
i
n
r
e
m
o
t
e
s
e
n
s
i
n
g
a
n
a
l
y
z
es
u
n
m
a
n
n
e
d
a
e
r
ia
l
v
e
h
i
c
l
e
(
U
AV
)
a
n
d
s
a
t
e
ll
i
t
e
th
e
r
m
a
l
i
m
a
g
e
r
y
f
o
r
n
e
a
r
r
e
a
l
-
t
i
m
e
UH
I
m
o
n
i
t
o
r
i
n
g
[
1
7
]
.
C
l
o
u
d
c
o
m
p
u
t
i
n
g
s
c
a
l
es
A
I
t
o
h
a
n
d
l
e
l
a
r
g
e
d
a
t
a
a
n
d
s
u
p
p
o
r
t
r
e
a
l
-
t
i
m
e
u
r
b
a
n
p
l
a
n
n
i
n
g
.
T
h
i
s
k
e
e
p
s
A
I
c
e
n
t
r
al
t
o
m
a
n
a
g
i
n
g
U
H
I
s
as
ci
t
i
es
g
r
o
w
a
n
d
c
l
i
m
a
t
e
c
h
a
n
g
e
s
[
1
8
]
.
2.
L
I
T
E
R
AT
U
RE
V
I
E
W
T
eh
r
an
i
et
a
l.
[
1
9
]
ex
p
l
o
r
es
th
e
u
s
e
o
f
th
e
ad
v
a
n
ce
d
ML
m
o
d
els
-
e.
g
.
,
g
ated
r
ec
u
r
r
en
t
u
n
its
(
GR
U
)
,
DNN,
an
d
ar
tific
ial
n
eu
r
al
n
et
wo
r
k
s
(
ANN
)
to
lear
n
to
p
r
e
d
i
ct
th
e
UHI
ef
f
ec
t
in
E
u
r
o
p
ea
n
cities.
I
t
b
u
ild
s
u
p
o
n
u
n
iv
ar
iate
d
atasets
f
r
o
m
6
9
ci
ties
f
o
r
2
0
0
7
-
2
0
2
1
(
n
=0
.
5
6
G
g
)
,
p
r
o
jectin
g
to
2
0
5
0
,
2
0
8
0
an
d
u
s
in
g
th
is
an
d
r
elate
d
d
atasets
f
o
r
th
e
p
u
r
p
o
s
e
o
f
u
n
d
er
s
tan
d
in
g
th
e
r
o
les
o
f
u
r
b
a
n
m
o
r
p
h
o
lo
g
y
(
s
p
atial
an
d
s
tr
u
ctu
r
al
f
ea
tu
r
es
o
f
u
r
b
a
n
lan
d
s
ca
p
es)
to
UHI
in
ten
s
ity
.
T
h
e
s
tu
d
y
f
in
d
s
GR
U
h
ig
h
ly
ac
cu
r
ate
an
d
s
h
o
ws
d
en
s
er
u
r
b
a
n
f
o
r
m
s
in
cr
ea
s
e
UHI
.
Yan
g
et
a
l.
[
2
0
]
ad
d
r
ess
es
th
e
cr
itically
im
p
o
r
tan
t
p
r
o
b
lem
o
f
th
e
ef
f
ec
t
o
f
th
e
UHI
o
n
b
u
ild
i
n
g
en
er
g
y
u
s
e.
UHI
is
a
p
h
en
o
m
en
o
n
wh
er
eb
y
u
r
b
an
ize
d
ar
ea
s
ex
h
i
b
it
a
tem
p
er
atu
r
e
h
ig
h
e
r
th
an
s
u
r
r
o
u
n
d
in
g
r
u
r
al
ar
ea
s
,
th
e
h
ea
tin
g
o
f
b
u
ild
i
n
g
s
in
cr
ea
s
es
en
er
g
y
d
em
an
d
,
esp
ec
ially
co
o
lin
g
o
v
er
war
m
e
r
m
o
n
t
h
s
.
I
t
s
tr
ess
e
s
ac
cu
r
ate
UHI
ass
es
s
m
en
t f
o
r
s
u
s
tain
ab
l
e
d
ev
elo
p
m
en
t.
Ass
af
et
a
l.
[
2
1
]
p
r
ed
ict
UHI
s
ev
er
ity
u
s
in
g
B
ay
esian
n
etw
o
r
k
s
ap
p
lied
to
f
in
e
g
r
ain
e
d
,
c
en
s
u
s
tr
ac
t
lev
el
d
ata.
T
h
is
q
u
an
tifie
s
UHI
im
p
ac
ts
ac
r
o
s
s
v
ar
y
in
g
d
e
n
s
ity
an
d
v
eg
etatio
n
.
T
h
e
s
tu
d
y
u
tili
ze
s
B
ay
esian
n
etwo
r
k
s
to
im
p
r
o
v
e
p
r
ed
ictiv
e
ac
cu
r
ac
y
b
y
m
o
d
elin
g
th
e
p
r
o
b
ab
ili
s
tic
r
elatio
n
s
h
ip
s
b
etwe
en
th
ese
f
ac
to
r
s
an
d
th
er
eb
y
to
p
r
o
v
id
e
a
f
in
er
g
r
ai
n
ed
ap
p
r
o
ac
h
to
th
e
s
tu
d
y
o
f
UHI
p
atter
n
s
.
3.
M
AT
E
R
I
AL
S
AND
M
E
T
H
O
D
T
h
e
UHI
ef
f
ec
t,
wh
er
ein
u
r
b
an
ar
ea
s
h
av
e
h
ig
h
er
tem
p
er
a
tu
r
es
th
an
s
u
r
r
o
u
n
d
in
g
r
u
r
al
r
eg
io
n
s
,
is
in
v
esti
g
ated
in
th
is
s
tu
d
y
.
T
h
e
m
ajo
r
r
ea
s
o
n
f
o
r
th
is
tem
p
er
at
u
r
e
d
is
p
ar
ity
is
p
r
im
ar
ily
u
r
b
a
n
f
ea
tu
r
es
in
clu
d
in
g
im
p
er
v
io
u
s
s
u
r
f
ac
es
s
u
ch
as
p
av
ed
r
o
ad
s
a
n
d
co
n
c
r
ete
s
tr
u
ct
u
r
es,
wh
ich
ab
s
o
r
b
a
n
d
r
etain
h
ea
t.
Ad
d
itio
n
ally
,
ex
ce
s
s
h
ea
t is r
elea
s
ed
f
r
o
m
v
eh
icles a
s
well
as h
ea
tin
g
an
d
co
o
lin
g
s
y
s
tem
s
[
2
2
]
.
3
.
1
.
Study
a
re
a
T
h
is
s
tu
d
y
u
s
ed
th
e
g
lo
b
al
U
HI
d
atasets
f
r
o
m
2
0
1
3
f
r
o
m
th
e
NASA
SEDA
C
[
2
2
]
an
d
in
c
lu
d
ed
th
eir
L
ST
m
ea
s
u
r
em
en
ts
in
d
eg
r
e
es
C
elsi
u
s
.
Ur
b
an
ar
ea
s
'
av
er
ag
e
s
u
m
m
e
r
d
a
y
tim
e
m
ax
i
m
u
m
s
an
d
n
ig
h
ttime
m
in
im
u
m
s
,
alo
n
g
with
th
e
u
r
b
an
r
u
r
al
tem
p
er
atu
r
e
d
if
f
e
r
en
ce
ar
e
in
cl
u
d
ed
in
t
h
e
d
ataset.
Ur
b
an
ex
ten
ts
ar
e
d
ef
in
ed
as
10
km
b
u
f
f
er
zo
n
es a
r
o
u
n
d
ea
ch
ar
ea
.
T
h
e
g
lo
b
al
r
u
r
al
-
u
r
b
an
m
a
p
p
in
g
p
r
o
ject
(
GR
UM
Pv
1
)
is
u
s
ed
to
d
er
iv
e
th
e
u
r
b
an
b
o
u
n
d
ar
ie
s
wh
ile
th
e
tem
p
er
atu
r
e
d
ata
ar
e
d
er
iv
ed
f
r
o
m
p
a
r
ts
o
f
th
e
m
o
d
er
ate
r
eso
lu
tio
n
im
ag
in
g
s
p
ec
tr
o
r
ad
io
m
ete
r
(
M
ODI
S)
o
n
NASA'
s
Aq
u
a
s
atell
ite.
T
h
is
d
ataset
is
g
eo
g
r
ap
h
ic
ally
wid
e
co
v
er
i
n
g
m
u
ltip
le
co
n
tin
en
ts
f
r
o
m
v
ar
y
in
g
p
o
r
tio
n
s
o
f
No
r
th
a
n
d
So
u
th
Am
er
ica,
Af
r
ica,
Asi
a,
E
u
r
o
p
e,
an
d
Oce
an
ia.
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6
°S
,
a
n
d
lo
n
g
itu
d
e
1
7
9
.
4
6
°E
to
-
1
7
6
.
2
°W
,
p
r
o
v
id
in
g
a
g
l
o
b
al
ex
am
p
le
o
f
th
e
UHI
ef
f
ec
t.
T
em
p
er
atu
r
e
d
ata
wer
e
r
ec
o
r
d
e
d
o
v
e
r
4
0
-
d
a
y
p
e
r
io
d
d
u
r
in
g
th
e
p
ea
k
o
f
s
u
m
m
er
m
o
n
th
s
f
r
o
m
J
u
ly
to
Au
g
u
s
t
in
th
e
n
o
r
th
er
n
h
em
is
p
h
er
e,
an
d
f
r
o
m
J
an
u
a
r
y
to
Feb
r
u
ar
y
in
th
e
s
o
u
th
er
n
h
em
is
p
h
er
e.
3
.
2
.
Da
t
a
c
o
llect
io
n a
nd
pre
-
pro
ce
s
s
ing
3
.
2
.
1
.
Da
t
a
co
llect
io
n
Fo
r
th
is
s
tu
d
y
,
th
e
d
ata
u
s
ed
in
it
co
m
es
f
r
o
m
th
e
g
lo
b
al
UHI
d
ata
s
et,
2
0
1
3
f
r
o
m
NASA
SEDA
C
,
C
en
ter
f
o
r
I
n
ter
n
atio
n
al
E
ar
th
Scien
ce
I
n
f
o
r
m
atio
n
Netwo
r
k
(
C
I
E
SIN
)
,
C
o
lu
m
b
ia
Un
iv
e
r
s
ity
[
2
2
]
.
T
h
e
L
ST
v
alu
es a
r
e
av
ailab
le
as th
is
d
ataset,
wh
ich
ar
e
lan
d
L
ST
v
alu
es f
o
r
u
r
b
a
n
an
d
r
u
r
al
ar
ea
s
d
e
r
iv
ed
f
r
o
m
MO
DI
S
s
atellite
d
ata.
Du
r
in
g
p
ea
k
s
u
m
m
er
m
o
n
th
s
t
h
e
d
ataset
co
n
t
ain
s
tem
p
er
atu
r
e
r
ea
d
in
g
s
f
o
r
d
ay
tim
e
m
a
x
im
u
m
as
well
as
n
ig
h
ttime
m
in
im
u
m
tem
p
er
atu
r
es.
As
a
p
r
ev
iew,
T
ab
le
1
p
r
esen
ts
s
o
m
e
o
f
th
e
s
am
p
le
en
tr
ies
in
th
e
d
ataset.
T
h
is
ass
is
ts
to
ex
p
lain
th
e
n
atu
r
e
o
f
d
e
m
o
g
r
a
p
h
ic
an
d
g
eo
g
r
ap
h
ic
in
f
o
r
m
atio
n
en
clo
s
ed
in
th
e
an
aly
s
is
.
T
h
e
d
ata
s
et
u
s
ed
in
th
is
s
tu
d
y
also
in
clu
d
es
u
r
b
a
n
a
r
ea
co
d
e
s
,
n
am
es
,
an
d
esti
m
ated
p
o
p
u
latio
n
s
,
wh
ich
f
o
r
m
th
e
b
asis
f
o
r
s
am
p
le
p
o
p
u
latio
n
esti
m
ates.
T
ab
le
1
.
A
p
r
ev
iew
o
f
d
ataset
u
s
ed
in
r
esear
ch
wo
r
k
I
S
O
U
R
B
I
D
I
S
O
3
U
R
B
I
D
N
A
M
E
S
C
H
N
M
ES9
0
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O
P
ES9
5
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O
P
ES0
0
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G
R
L8
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R
L
8
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p
e
r
n
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k
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ER
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V
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K
9
1
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5
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1
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S
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1
5
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S
A
15
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a
r
r
o
w
B
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R
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W
3
4
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9
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o
n
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O
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G
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19
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v
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n
d
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A
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S
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N
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1
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1
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6
3
N
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2
1
NOR
21
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j
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f
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r
d
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R
D
1
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1
1
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5
1
0
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9
N
O
R
2
6
NOR
26
B
e
r
l
e
v
å
g
B
ER
LEV
A
G
1
2
9
7
1
2
4
7
1
1
7
4
G
R
L3
1
G
R
L
31
U
u
mm
a
n
n
a
q
U
U
M
M
A
N
N
A
Q
1
4
0
5
1
4
2
2
1
4
4
0
R
U
S
4
0
R
U
S
40
A
n
a
d
y
r
ANADYR
1
2
0
5
4
1
2
0
3
5
1
1
9
0
0
W
LF5
2
W
LF
52
M
a
t
a
'
u
t
u
M
A
TA
U
TU
1
2
2
2
1
1
5
0
1
0
8
3
3
.
2
.
2
.
Da
t
a
pre
-
pro
ce
s
s
ing
W
e
p
r
ep
r
o
ce
s
s
ed
th
e
d
ataset
f
o
r
m
o
d
elin
g
.
I
n
th
e
p
r
esen
ce
o
f
h
ig
h
clo
u
d
co
v
er
,
m
is
s
in
g
tem
p
er
atu
r
e
v
alu
es
wer
e
f
illed
with
alter
n
ativ
e
p
er
io
d
d
ata
(
Ap
r
il
t
o
Ma
y
2
0
1
3
f
o
r
t
h
e
n
o
r
th
e
r
n
h
e
m
is
p
h
er
e,
an
d
Dec
em
b
er
2
0
1
3
to
J
an
u
ar
y
2
0
1
4
f
o
r
t
h
e
s
o
u
th
e
r
n
h
em
is
p
h
er
e)
[
2
2
]
.
W
e
e
x
clu
d
ed
r
eg
i
o
n
s
with
o
u
t
UHI
o
r
u
r
b
an
ar
ea
s
.
W
e
m
ap
p
ed
an
d
ag
g
r
e
g
ated
u
r
b
an
-
r
u
r
al
tem
p
e
r
atu
r
es.
Seaso
n
al
d
ata
wer
e
alig
n
ed
ac
r
o
s
s
h
em
is
p
h
er
es.
No
r
m
aliza
tio
n
r
ed
u
ce
d
g
eo
g
r
a
p
h
ic/clim
atic
b
ias.
3
.
2
.
3
.
Wo
rkf
lo
w
T
h
e
p
r
im
ar
y
task
o
f
th
is
wo
r
k
is
to
p
r
ed
ict
th
e
UHI
ef
f
ec
t,
i.e
.
th
e
tem
p
er
atu
r
e
d
if
f
er
en
ce
b
e
twee
n
th
e
u
r
b
an
an
d
s
u
r
r
o
u
n
d
in
g
r
u
r
al
ar
ea
(
D_
T
_
DI
FF
)
an
d
at
n
i
g
h
t
(
N_
T
_
DI
FF
)
.
W
e
u
s
e
a
d
ataset
in
clu
d
in
g
u
r
b
a
n
an
d
b
u
f
f
er
ar
ea
tem
p
er
atu
r
e
o
b
s
er
v
atio
n
s
,
u
r
b
an
ar
ea
s
ize,
p
o
p
u
latio
n
esti
m
ates,
g
eo
g
r
ap
h
i
ca
l
co
o
r
d
in
ates
,
to
ac
h
iev
e
th
is
.
T
h
e
tem
p
er
atu
r
e
d
if
f
er
en
ce
s
ar
e
p
r
ed
icted
u
s
in
g
a
c
o
llectio
n
o
f
m
u
ltip
le
ML
m
o
d
els
co
n
s
is
tin
g
o
f
RF
-
r
eg
r
ess
o
r
,
ex
tr
em
e
g
r
a
d
ien
t
b
o
o
s
tin
g
(
XGBo
o
s
t
)
-
r
eg
r
ess
o
r
,
lig
h
t
g
r
ad
ien
t
b
o
o
s
tin
g
m
a
ch
in
e
(
L
i
g
h
tGB
M
)
,
m
u
ltil
ay
er
p
er
ce
p
tr
o
n
(
ML
P)
,
an
d
L
STM
with
atten
tio
n
m
ec
h
an
is
m
o
n
th
ese
f
ea
tu
r
es.
Fin
a
lly
,
we
ev
alu
ate
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
m
o
d
el
u
s
in
g
d
if
f
er
e
n
t
er
r
o
r
m
etr
ics
s
u
ch
a
s
r
o
o
t
m
ea
n
s
q
u
ar
ed
e
r
r
o
r
(
R
MSE
)
,
m
ea
n
s
q
u
ar
e
d
er
r
o
r
(
MSE
)
,
m
ea
n
ab
s
o
lu
te
er
r
o
r
(
MA
E
)
,
an
d
co
e
f
f
icien
t
o
f
d
eter
m
in
atio
n
(
R²
)
(
to
en
s
u
r
e
g
o
o
d
p
r
e
d
ictio
n
s
o
f
th
e
UHI
ef
f
ec
t a
cr
o
s
s
th
e
o
th
e
r
r
eg
io
n
s
)
.
Fig
u
r
e
1
r
ep
r
esen
ts
th
e
f
lo
wch
ar
t
o
f
th
e
r
esear
ch
.
3
.
2
.
4
.
L
a
nd
s
ca
pe
m
et
rics
L
an
d
s
ca
p
e
m
etr
ics
in
u
r
b
an
s
tu
d
ies
an
d
en
v
ir
o
n
m
e
n
tal
s
cien
ce
ar
e
im
p
o
r
tan
t
to
q
u
an
ti
f
y
s
p
atial
p
atter
n
s
in
lan
d
s
ca
p
e
f
ea
tu
r
es
f
o
r
d
escr
ib
in
g
u
n
d
er
l
y
in
g
e
n
v
ir
o
n
m
e
n
tal
p
h
en
o
m
en
a
s
u
ch
as
th
e
UHI
ef
f
ec
t.
I
n
p
r
ed
ictin
g
UHI
,
lan
d
s
ca
p
e
m
etr
ics
q
u
an
tif
y
th
ese
ch
ar
ac
t
er
is
tics
o
f
th
e
u
r
b
an
ar
ea
an
d
s
u
r
r
o
u
n
d
i
n
g
lan
d
:
in
clu
d
in
g
lan
d
u
s
e
ty
p
es,
lan
d
co
v
er
,
an
d
s
p
atial
co
n
f
i
g
u
r
ati
o
n
d
ir
ec
tly
in
f
lu
e
n
cin
g
th
e
th
er
m
al
p
r
o
p
e
r
ties
o
f
th
e
s
tu
d
y
ar
ea
.
Sev
er
al
lan
d
s
ca
p
e
m
etr
ics
ar
e
u
s
ed
to
ch
ar
ac
ter
ize
th
e
s
tu
d
ied
u
r
b
an
ar
ea
s
an
d
th
eir
ass
o
ciate
d
1
0
k
m
b
u
f
f
er
zo
n
es.
T
h
ese
m
e
tr
ics in
clu
d
e:
‒
Ur
b
an
a
r
ea
s
ize
(
SQKM_
FIN
AL
)
:
a
c
o
m
p
ar
is
o
n
was
m
a
d
e
o
f
th
e
ex
te
n
t
in
s
q
u
ar
e
k
ilo
m
et
r
es
th
at
d
e
f
in
es
'
u
r
b
an
'
an
d
th
e
im
p
ac
t
it
h
as
o
n
th
e
lo
ca
l
tem
p
er
atu
r
e
v
ar
iati
o
n
.
I
m
p
e
r
v
io
u
s
s
u
r
f
ac
es
in
lar
g
er
u
r
b
a
n
ar
ea
s
co
v
er
m
o
r
e
s
u
r
f
ac
e
ar
ea
th
at
r
esu
lts
in
a
m
o
r
e
s
ig
n
if
ican
t U
HI
ef
f
ec
t
[
1
]
.
‒
Po
p
u
latio
n
esti
m
a
tes
(
E
S9
5
POP):
s
in
ce
a
s
ig
n
if
ican
t
co
n
tr
i
b
u
to
r
to
waste
h
ea
t
in
u
r
b
an
ar
ea
s
is
th
e
lev
el
o
f
h
u
m
an
ac
tiv
ity
,
th
e
esti
m
at
ed
p
o
p
u
latio
n
o
f
u
r
b
a
n
ar
ea
s
i
n
1
9
9
5
is
u
s
ed
as
p
r
o
x
y
f
o
r
it.
T
h
e
UHI
ef
f
ec
t
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
I
n
teg
r
a
tin
g
ma
c
h
in
e
lea
r
n
in
g
a
n
d
d
e
ep
lea
r
n
in
g
w
ith
la
n
d
s
c
a
p
e
metrics
fo
r
u
r
b
a
n
h
ea
t
(
S
i
d
d
h
a
r
th
P
a
l)
4831
is
o
f
ten
ass
o
ciate
d
with
h
ig
h
e
r
p
o
p
u
latio
n
d
e
n
s
ities
with
co
r
r
esp
o
n
d
i
n
g
h
eig
h
ten
e
d
e
n
er
g
y
co
n
s
u
m
p
tio
n
an
d
h
ea
t
p
r
o
d
u
ctio
n
[
7
]
.
‒
T
em
p
er
atu
r
e
v
ar
iab
ilit
y
(
UR
B
_
D_
ME
AN,
B
UF_
D_
ME
A
N,
UR
B
_
N_
ME
AN,
B
U
F_
N
_
ME
AN)
:
th
e
u
r
b
an
a
n
d
its
b
u
f
f
er
a
r
e
d
ef
in
ed
b
y
a
b
u
f
f
er
ar
ea
s
u
r
r
o
u
n
d
i
n
g
th
e
u
r
b
an
ar
ea
at
a
s
ca
le
o
f
1
:1
0
,
0
0
0
,
an
d
th
ese
m
etr
ics
r
ep
r
esen
t
th
e
a
v
er
ag
e
d
ay
tim
e
m
a
x
im
u
m
an
d
av
er
ag
e
n
ig
h
ttime
m
in
im
u
m
L
ST
s
f
o
r
u
r
b
a
n
ar
ea
an
d
its
b
u
f
f
er
.
Dir
ec
t
in
d
icato
r
s
o
f
th
e
UHI
ef
f
ec
t
a
n
d
th
e
m
ea
n
in
g
o
f
th
e
t
h
er
m
al
lan
d
s
ca
p
e
p
er
f
o
r
m
an
ce
a
r
e
D_
T
_
DI
FF
(
d
ay
)
an
d
N_
T
_
DI
FF
(
n
ig
h
t)
,
th
e
tem
p
er
atu
r
e
d
if
f
er
en
ce
s
b
etwe
en
u
r
b
an
an
d
r
u
r
al
ar
ea
s
[
2
2
]
.
‒
Geo
g
r
ap
h
ic
l
o
ca
tio
n
(
L
AT
I
T
UDE
,
L
ONGI
T
UDE
)
:
g
eo
g
r
ap
h
ic
lo
ca
ti
o
n
o
f
th
e
u
r
b
an
ar
ea
s
is
co
r
r
esp
o
n
d
ed
t
o
th
e
clim
atic
co
n
d
itio
n
o
f
th
e
r
eg
io
n
b
ec
a
u
s
e
tem
p
er
atu
r
e
v
a
r
iatio
n
is
d
e
p
en
d
en
t
o
n
t
h
e
latitu
d
e,
altitu
d
e,
a
n
d
n
ea
r
n
ess
to
m
o
u
n
tain
s
o
r
b
o
d
ies o
f
wat
er
.
L
o
ca
lly
,
th
ese
g
e
o
g
r
a
p
h
ic
c
h
ar
ac
ter
is
tics
p
lay
a
m
ajo
r
r
o
le
in
lo
ca
l d
if
f
e
r
en
ce
s
in
tem
p
er
atu
r
e
b
etwe
en
u
r
b
an
an
d
r
u
r
al
a
r
ea
s
[
2
3
]
.
‒
L
an
d
co
v
er
a
n
d
im
p
er
v
io
u
s
s
u
r
f
ac
e
ar
ea
:
wh
ile
n
o
t
ex
p
licitly
a
p
ar
t
o
f
th
is
d
ataset,
th
e
p
er
ce
n
tag
e
o
f
p
av
e
d
r
o
ad
s
,
b
u
ild
in
g
s
,
etc.
an
d
lan
d
co
v
er
ty
p
es
(
f
o
r
ex
am
p
le
g
r
ee
n
s
p
ac
e,
wate
r
b
o
d
y
)
o
f
an
a
r
e
a
also
p
lay
s
a
m
ajo
r
r
o
le
in
t
h
e
UHI
ef
f
ec
t.
T
h
ese
v
ar
iab
les
ca
n
b
e
d
er
iv
ab
le
f
r
o
m
s
atellite
im
ag
er
y
o
r
o
th
er
s
p
atial
d
atasets
an
d
cr
u
cial
f
o
r
u
n
d
er
s
tan
d
in
g
th
e
r
ea
s
o
n
s
f
o
r
d
if
f
er
e
n
ce
s
in
tem
p
er
atu
r
es b
etwe
en
th
e
u
r
b
an
an
d
r
u
r
al
zo
n
es
[
2
4
]
,
[
2
5
]
.
Fig
u
r
e
1
.
Dem
o
n
s
tr
atin
g
wo
r
k
f
lo
w
o
f
r
esear
c
h
wo
r
k
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
4
,
No
.
6
,
Dec
em
b
er
2
0
2
5
:
4
8
2
8
-
4
8
3
7
4832
4.
RE
SU
L
T
AND
DI
SCUS
SI
O
N
U
H
I
h
as
b
e
e
n
a
n
a
l
y
z
e
d
v
i
s
u
r
b
a
n
-
r
u
r
a
l
t
e
m
p
e
r
a
t
u
r
e
d
i
f
f
e
r
e
n
c
es
a
n
d
c
o
m
p
a
r
e
d
M
L
m
o
d
el
s
.
Ou
r
g
o
a
l
is
t
o
f
i
n
d
UH
I
p
a
t
te
r
n
s
a
n
d
p
r
e
d
i
ct
i
v
e
m
o
d
e
ls
f
o
r
u
r
b
a
n
p
l
a
n
n
i
n
g
.
F
o
r
t
h
e
e
v
a
l
u
a
ti
o
n
,
w
e
a
d
o
p
t
e
d
k
e
y
s
c
o
r
es
,
n
a
m
el
y
R
²
,
R
MS
E
,
MA
E
,
a
n
d
MS
E
t
o
m
e
a
s
u
r
e
t
h
e
p
r
o
p
o
r
t
i
o
n
o
f
v
a
r
i
a
n
c
e
p
r
e
d
i
c
te
d
b
y
t
h
e
m
o
d
e
l
,
m
e
a
n
m
a
g
n
i
t
u
d
e
o
f
e
r
r
o
r
s
,
m
e
a
n
a
b
s
o
l
u
te
d
i
f
f
e
r
e
n
ce
o
f
p
r
e
d
i
c
t
e
d
a
n
d
a
c
t
u
a
l
v
al
u
es
a
n
d
t
h
e
MS
E
o
f
p
r
e
d
i
c
t
i
o
n
r
es
p
e
c
t
i
v
el
y
.
4
.
1
.
E
x
plo
ra
t
o
ry
da
t
a
a
na
l
y
s
is
E
x
p
l
o
r
a
t
o
r
y
d
a
t
a
a
n
a
l
y
s
is
(
E
DA
)
h
a
s
b
e
e
n
p
e
r
f
o
r
m
e
d
t
o
i
d
e
n
ti
f
y
p
a
t
t
e
r
n
s
i
m
p
o
r
t
a
n
t
f
o
r
p
r
e
d
i
c
t
i
n
g
U
H
I
.
W
e
a
n
a
l
y
ze
d
d
i
s
t
r
i
b
u
t
i
o
n
s
o
f
n
u
m
e
r
i
c
v
a
r
i
a
b
l
es
a
s
s
h
o
w
n
i
n
Fig
u
r
e
2
.
S
o
m
e
f
e
a
t
u
r
e
s
we
r
e
s
k
ew
e
d
,
o
t
h
e
r
s
n
o
r
m
a
l
;
t
h
u
s
f
e
a
t
u
r
e
s
c
al
i
n
g
wa
s
a
p
p
l
ie
d
.
F
i
g
u
r
e
3
s
h
o
w
s
f
e
a
t
u
r
e
s
ca
t
t
e
r
p
l
o
ts
a
n
d
r
el
a
ti
o
n
s
h
i
p
s
.
W
e
w
a
n
t
e
d
t
o
k
n
o
w
w
h
e
t
h
e
r
t
h
e
r
e
is
d
i
f
f
e
r
e
n
c
e
b
etw
e
e
n
c
l
u
s
t
e
r
e
d
a
r
e
as
a
n
d
b
u
f
f
e
r
a
r
e
a
s
f
o
r
s
o
m
e
f
ea
t
u
r
e
p
a
i
r
s
;
a
n
d
f
o
r
s
o
m
e
,
u
r
b
a
n
r
e
g
i
o
n
s
h
a
v
e
d
i
f
f
e
r
e
n
t
d
e
g
r
e
e
o
f
c
l
u
s
t
e
r
s
c
o
m
p
a
r
e
d
t
o
b
u
f
f
e
r
a
r
e
a
s
,
m
e
a
n
i
n
g
t
h
a
t
u
r
b
a
n
i
z
a
t
io
n
h
a
s
a
n
e
f
f
e
c
t
o
n
s
o
m
e
o
f
t
h
e
f
e
at
u
r
e
p
a
i
r
s
.
D
et
e
c
t
e
d
c
o
r
r
e
la
t
i
o
n
s
,
l
i
k
e
b
e
t
w
ee
n
t
e
m
p
e
r
a
t
u
r
e
a
n
d
b
u
i
lt
-
u
p
d
e
n
s
i
t
y
,
al
i
g
n
w
it
h
U
H
I
t
r
e
n
d
s
.
Fig
u
r
e
2
.
T
h
e
p
lo
t sh
o
ws d
is
tr
ib
u
tio
n
o
f
n
u
m
er
ic
attr
ib
u
tes th
r
o
u
g
h
o
u
t th
e
d
ataset
Fig
u
r
e
3
.
Pair
wis
e
r
elatio
n
s
h
ip
s
o
f
d
ata
f
ea
tu
r
es
(
Pair
p
lo
t)
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
I
n
teg
r
a
tin
g
ma
c
h
in
e
lea
r
n
in
g
a
n
d
d
e
ep
lea
r
n
in
g
w
ith
la
n
d
s
c
a
p
e
metrics
fo
r
u
r
b
a
n
h
ea
t
(
S
i
d
d
h
a
r
th
P
a
l)
4833
4.
2
.
M
o
del per
f
o
rma
nce
co
m
pa
riso
n
T
h
e
ev
al
u
atio
n
o
f
ML
m
o
d
els
f
o
r
UHI
p
r
e
d
ictio
n
u
s
in
g
R
M
SE,
MSE
,
an
d
MA
E
h
as
b
ee
n
d
o
n
e.
We
an
aly
s
ed
th
e
p
er
f
o
r
m
an
ce
o
f
f
iv
e
m
o
d
els:
RF
-
r
eg
r
ess
o
r
,
X
GB
o
o
s
t
-
r
eg
r
ess
o
r
,
L
ig
h
tGB
M,
ML
P
,
an
d
L
STM
with
an
atten
tio
n
m
ec
h
an
is
m
.
T
h
e
L
STM
with
atten
tio
n
was
th
e
b
est
p
er
f
o
r
m
in
g
m
o
d
el
an
d
is
p
r
esen
ted
as
f
o
llo
win
g
th
e
lo
west
R
MSE
,
eq
u
al
to
0
.
0
2
5
3
(
d
ay
)
,
0
.
0
3
1
4
(
n
i
g
h
t)
an
d
MA
E
,
eq
u
a
l
to
0
.
0
0
7
9
(
d
a
y
)
,
0
.
0
1
1
3
(
n
ig
h
t)
,
an
d
th
e
h
i
g
h
est
R
²
v
alu
es,
eq
u
al
to
0
.
9
9
9
8
(
d
ay
)
,
0
.
9
9
9
2
(
n
ig
h
t)
.
T
h
e
ML
P
also
s
h
o
wed
g
o
o
d
r
esu
lts
with
R
²=
0
.
9
9
7
3
(
d
a
y
)
a
n
d
0
.
9
9
7
5
(
n
ig
h
t)
an
d
R
MSE
=
0
.
0
9
8
6
(
d
ay
)
,
0
.
0
5
5
7
(
n
ig
h
t)
.
RF
an
d
L
ig
h
tGB
M
b
r
o
u
g
h
t e
x
ce
llen
t r
esu
lts
,
an
d
RF
g
o
t th
e
h
ig
h
est R
²
v
alu
es o
f
0
.
9
9
1
7
(
d
a
y
)
an
d
0
.
9
6
8
3
(
n
ig
h
t)
with
th
e
lo
west
R
MSE
o
f
0
.
1
7
2
6
an
d
0
.
1
9
8
6
L
ig
h
tGB
M
ac
h
iev
ed
R
²
v
alu
e
s
0
.
9
7
3
6
(
d
a
y
)
an
d
0
.
9
3
0
2
(
n
ig
h
t)
an
d
R
MSE
o
f
0
.
3
0
8
1
an
d
0
.
2
9
4
8
.
C
lo
s
ely
b
eh
in
d
was
th
e
XGBo
o
s
t,
wh
ich
h
ad
s
lig
h
tly
wo
r
s
e
ac
cu
r
ac
y
o
f
R
²
eq
u
al
to
0
.
9
7
3
1
(
d
ay
)
an
d
0
.
9
6
3
8
(
n
ig
h
t)
an
d
R
MSE
eq
u
al
to
0
.
3
1
0
9
an
d
0
.
2
1
2
2
r
esp
ec
tiv
ely
.
T
h
e
F
ig
u
r
e
4
r
ep
r
esen
ts
th
e
co
m
p
a
r
is
o
n
o
f
all
m
o
d
els.
Fig
u
r
e
5
r
ep
r
esen
ts
th
e
co
m
p
ar
is
o
n
o
f
th
e
s
ca
tter
p
lo
t
o
f
th
e
m
o
d
els.
Fig
u
r
e
5
s
h
o
ws
ac
tu
al
v
s
.
p
r
ed
icted
v
alu
es
s
ca
tter
p
lo
ts
o
f
ea
ch
m
o
d
el:
Fig
u
r
e
5
(
a)
RF
is
m
o
d
er
ately
ac
cu
r
ate;
Fig
u
r
e
5
(
b
)
XGBo
o
s
t
also
p
er
f
o
r
m
s
s
im
ilar
ly
with
th
e
p
er
f
o
r
m
a
n
ce
h
av
in
g
a
to
u
c
h
m
o
r
e
d
is
p
er
s
io
n
;
Fig
u
r
e
5
(
c
)
L
ig
h
tGB
M
is
m
o
r
e
s
ca
tter
ed
an
d
h
as
a
l
o
wer
R
2
;
Fig
u
r
e
5
(
d
)
ML
P
h
as
a
h
ig
h
class
if
icatio
n
ac
cu
r
ac
y
;
an
d
Fig
u
r
e
5
(
e)
L
S
T
M
with
atten
tio
n
p
r
esen
ts
th
e
f
itti
n
g
p
o
in
ts
at
a
clo
s
e
d
is
ta
n
ce
to
th
e
d
iag
o
n
al.
T
h
ese
p
lo
ts
d
escr
ib
e
th
e
p
r
ed
i
ctiv
e
p
er
f
o
r
m
an
ce
o
f
ea
ch
o
f
t
h
ese
m
o
d
els.
Fig
u
r
e
4
.
A
b
ar
ch
a
r
t o
f
th
e
m
etr
ics p
er
f
o
r
m
e
d
ac
r
o
s
s
th
e
d
a
y
an
d
n
ig
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er
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m
an
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4
.
3
.
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y
v
s
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nig
ht
perf
o
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m
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e
l
s
p
er
f
o
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m
e
d
b
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t
t
e
r
d
u
r
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g
t
h
e
d
a
y
t
im
e
w
i
t
h
l
o
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er
e
r
r
o
r
s
.
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ay
t
i
m
e
d
a
t
a
a
r
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m
o
r
e
s
t
a
b
l
e
an
d
p
r
ed
i
c
t
a
b
le
.
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w
ev
e
r
,
p
r
ed
ic
t
i
o
n
e
r
r
o
r
s
w
e
r
e
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h
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r
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d
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t
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th
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t
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ed
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s
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o
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ch
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ac
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s
tr
i
b
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t
i
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o
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v
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e
ta
t
i
o
n
.
F
ig
u
r
e
6
r
ep
r
e
s
en
t
s
t
h
e
t
em
p
er
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u
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en
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ay
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n
d
n
i
g
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t
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o
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z
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ty
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o
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l
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er
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a
t
n
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g
h
t
.
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h
e
d
a
y
tim
e
tem
p
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atu
r
es
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if
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er
en
ce
s
in
th
e
k
er
n
el
d
e
n
s
ity
e
s
tim
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n
(
KDE
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p
lo
t
(
b
lu
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cu
r
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s
h
o
ws
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wid
er
d
is
tr
ib
u
tio
n
o
f
er
r
o
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s
with
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m
ax
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m
ar
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ze
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p
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r
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ar
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ilit
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as
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h
o
w
n
in
F
ig
u
r
e
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.
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h
is
wid
er
d
is
tr
ib
u
tio
n
i
s
in
d
icativ
e
o
f
p
o
o
r
er
p
er
f
o
r
m
an
ce
with
d
ay
tim
e
co
n
d
itio
n
s
,
wh
ich
m
ay
b
e
ex
p
lain
ed
b
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a
s
tr
o
n
g
er
d
ep
en
d
en
ce
o
n
atm
o
s
p
h
er
ic
d
y
n
am
ics
f
r
o
m
s
o
lar
r
a
d
iatio
n
o
r
lo
ca
lized
wea
th
er
ev
e
n
ts
,
f
o
r
in
s
tan
ce
.
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h
e
tails
s
h
o
w
s
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m
e
in
s
tan
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lar
g
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ten
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r
th
er
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t
u
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n
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(
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u
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ates
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u
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ig
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5.
CO
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O
N
Fo
r
p
r
ed
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f
f
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ts
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en
t
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m
o
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te
s
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in
th
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s
in
g
d
a
y
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d
n
i
g
h
t
tem
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er
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r
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d
ata.
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h
e
b
est
p
er
f
o
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in
g
m
o
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STM
with
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tio
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is
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o
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th
e
m
o
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test
ed
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t
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h
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west
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R
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ata'
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atter
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to
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atial
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o
r
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iatio
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e
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s
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ati
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o
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e
n
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ay
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ter
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ar
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o
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s
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els
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elativ
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to
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ig
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ttime
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r
e
d
ictio
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s
,
wh
ich
h
ig
h
lig
h
ts
th
e
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ay
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to
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ay
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o
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itio
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s
a
f
f
ec
ted
b
y
f
ac
to
r
s
s
u
ch
as
s
o
lar
r
ad
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n
,
u
r
b
an
g
eo
m
etr
y
,
an
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v
e
g
etatio
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o
v
er
.
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h
ttime
s
tab
ilit
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im
p
r
o
v
e
d
m
o
d
el
co
n
s
is
ten
cy
.
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atial
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s
is
s
h
o
ws
s
p
r
awl
v
ar
iatio
n
,
s
u
g
g
esti
n
g
lo
ca
l
s
o
l
u
tio
n
s
.
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STM
h
elp
s
p
lan
n
er
s
tar
g
et
h
ig
h
-
r
is
k
ar
ea
s
with
f
in
e
-
g
r
ain
ed
p
r
ed
ictio
n
s
.
T
h
er
e
ar
e
m
u
ltip
le
p
r
ac
tical
im
p
licatio
n
s
o
f
th
e
s
tu
d
y
:
u
r
b
an
p
lan
n
e
r
s
will
b
e
ab
le
to
lo
ca
te
h
ig
h
-
r
is
k
ar
ea
s
an
d
p
la
n
s
p
ec
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p
r
o
v
is
i
o
n
s
,
in
clu
d
in
g
t
h
e
en
lar
g
em
en
t
o
f
g
r
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n
s
p
ac
es
a
n
d
th
e
u
tili
s
atio
n
o
f
r
e
f
lectiv
e
m
ater
ials
.
L
im
itatio
n
s
in
clu
d
e
r
elian
ce
o
n
s
atellit
e
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ST,
wh
ich
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ay
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is
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m
icr
o
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r
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im
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ac
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u
r
ac
y
a
n
d
g
u
i
d
e
lo
ca
l U
HI
m
itig
atio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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F
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NC
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[
1
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J.
K
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.
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[
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[
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[
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[
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E.
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[
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[
9
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S
.
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