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e
p
er
ce
n
ta
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e
o
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v
ar
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s
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.
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t
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ies
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ticu
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atter
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m
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r
ea
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t
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m
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w
o
cl
u
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ter
p
ar
titi
o
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p
lain
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v
ar
io
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s
t
y
p
e
s
o
f
r
ain
f
all
p
atter
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s
.
T
u
k
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y
'
s
b
i
w
ei
g
h
t
co
r
r
elatio
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i
s
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ased
o
n
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u
k
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b
i
w
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f
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n
ct
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th
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r
elies
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n
M
-
e
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ato
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s
u
s
ed
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n
r
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u
s
t
co
r
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esti
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at
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h
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p
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m
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esis
ta
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t
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ata.
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o
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h
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m
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cc
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[
8
]
.
Ho
w
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,
in
P
C
A
b
ased
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k
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t is
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ased
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tio
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t
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r
i
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al
to
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tial
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ain
f
all
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ata
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P
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in
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Ma
la
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ia.
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h
e
p
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r
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ain
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al
l
p
atter
n
s
i
n
P
en
in
s
u
lar
Ma
la
y
s
ia.
2.
RE
S
E
ARCH
M
E
T
H
O
D
I
n
th
i
s
s
t
u
d
y
,
t
h
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s
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n
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cc
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r
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ce
o
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ex
tr
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e
r
ain
f
all
e
v
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t
d
escr
ib
ed
as
to
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tial
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ain
f
a
ll.
I
t
w
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ar
y
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tes
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r
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in
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m
/d
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ased
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teg
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o
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r
ain
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ten
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it
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y
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ab
atan
P
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g
air
an
d
an
Salira
n
(
J
P
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f
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tial c
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Fig
u
r
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1
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ail
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3
.
1
.
P
rincipa
l C
o
m
po
nent
A
na
ly
s
is
ba
s
ed
P
ea
rso
n Co
rr
e
la
t
io
n
P
C
A
i
s
d
esi
g
n
ed
to
r
ed
u
ce
t
h
e
n
u
m
b
er
o
f
v
ar
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les o
f
in
ter
e
s
t in
to
a
s
m
al
ler
s
et
o
f
co
m
p
o
n
en
ts
w
h
ile
r
etain
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n
g
m
o
s
t
o
f
t
h
e
s
ig
n
i
f
ica
n
t
i
n
f
o
r
m
atio
n
[
9
]
,
[
1
0
]
.
T
h
is
i
s
ac
h
iev
ed
b
y
co
n
v
e
r
ti
n
g
a
s
et
o
f
o
b
s
er
v
at
io
n
s
o
f
p
o
s
s
ib
l
y
co
r
r
elate
d
v
ar
iab
les
in
to
a
s
et
o
f
li
n
ea
r
l
y
u
n
co
r
r
elate
d
v
ar
iab
les
ca
lled
p
r
in
cip
al
co
m
p
o
n
en
ts
.
T
h
e
f
ir
s
t
p
r
in
cip
al
co
m
p
o
n
en
t
ac
co
u
n
ts
f
o
r
as
m
u
ch
o
f
t
h
e
v
ar
iat
io
n
i
n
t
h
e
o
r
ig
i
n
al
d
ata.
T
h
en
ea
ch
s
u
cc
ee
d
in
g
co
m
p
o
n
e
n
t
ac
co
u
n
ts
f
o
r
as
m
u
c
h
o
f
t
h
e
r
e
m
ai
n
in
g
v
ar
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s
u
b
j
ec
t
to
b
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u
n
co
r
r
elate
d
w
ith
t
h
e
p
r
ev
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u
s
co
m
p
o
n
e
n
t.
C
o
v
ar
ian
ce
o
r
co
r
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m
a
tr
ix
d
er
iv
ed
f
r
o
m
th
e
d
ata
m
atr
ix
p
la
y
s
an
i
m
p
o
r
tan
t
r
o
le
in
P
C
A
to
ca
lcu
late
it
s
eig
e
n
v
alu
e
s
an
d
eig
en
v
ec
to
r
s
to
o
b
ta
in
t
h
e
ass
o
ciate
d
co
m
p
o
n
e
n
ts
t
h
at
ac
co
u
n
t
f
o
r
m
o
s
t
o
f
th
e
v
ar
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n
s
i
n
t
h
e
d
ata
[
1
1
]
.
Fo
r
th
e
p
u
r
p
o
s
e
o
f
t
h
i
s
s
tu
d
y
,
co
r
r
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n
m
atr
ix
is
u
s
ed
.
I
t
is
g
en
er
all
y
r
ec
o
m
m
e
n
d
ed
tak
i
n
g
a
t
least
7
0
%
o
f
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m
u
lati
v
e
p
er
ce
n
tag
e
o
f
to
tal
v
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n
as
a
b
e
n
ch
m
ar
k
to
c
u
t
o
f
f
t
h
e
eig
en
v
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es
in
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lar
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e
d
ata
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et
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o
r
ex
tr
ac
tin
g
t
h
e
n
u
m
b
er
o
f
co
m
p
o
n
e
n
ts
[
1
2
]
.
T
h
e
r
ed
u
ce
d
m
a
tr
ix
i
s
t
h
e
co
m
p
o
n
e
n
t
m
atr
ix
o
f
e
ig
e
n
v
ec
to
r
―
lo
ad
in
g
s
‖
w
h
ic
h
d
e
f
i
n
es
th
e
n
e
w
v
ar
iab
le
s
co
n
s
i
s
ti
n
g
o
f
li
n
ea
r
tr
an
s
f
o
r
m
atio
n
o
f
t
h
e
o
r
ig
in
a
l v
ar
iab
les th
a
t
m
a
x
i
m
izes t
h
e
v
ar
ian
ce
in
t
h
e
n
e
w
a
x
es.
T
h
e
s
tep
s
in
v
o
l
v
ed
in
P
C
A
al
g
o
r
ith
m
ar
e
as
f
o
llo
w
s
:
Step
1
: O
b
tain
th
e
i
n
p
u
t
m
atr
i
x
.
Step
2
: Calcu
late
its
co
r
r
elatio
n
m
atr
ix
.
Step
3
: Calcu
late
th
e
ei
g
e
n
v
ec
to
r
s
a
n
d
eig
en
v
alu
e
s
o
f
t
h
e
co
r
r
elatio
n
m
atr
i
x
.
Step
4
: Sele
ct
th
e
m
o
s
t i
m
p
o
r
tan
t p
r
in
cip
al
co
m
p
o
n
e
n
ts
b
ased
o
n
c
u
m
u
lat
iv
e
p
er
ce
n
ta
g
e
o
f
to
tal
v
ar
iatio
n
.
Step
5
: D
er
iv
e
th
e
n
e
w
d
ata
s
et
Step
6
: Calcu
late
C
ali
n
s
k
i a
n
d
Har
ab
asz
in
d
ex
i
n
n
e
w
d
ata
s
et
to
d
eter
m
i
n
e
th
e
b
est
n
u
m
b
er
o
f
cl
u
s
ter
Step
7
: A
p
p
l
y
k
-
m
ea
n
s
m
et
h
o
d
to
n
ew
d
ata
s
et
Evaluation Warning : The document was created with Spire.PDF for Python.
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N:
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d
en
tifi
ca
tio
n
o
f
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in
fa
ll P
a
tter
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Hyd
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imu
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Usi
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(
S
.
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S
h
a
h
a
r
u
d
in
)
1165
3
.
2
.
P
rincipa
l C
o
m
po
nent
A
na
ly
s
is
ba
s
ed
T
u
key
’
s
B
iw
ei
g
ht
Co
rr
ela
t
io
n
P
C
A
b
ased
T
u
k
e
y
’
s
b
i
w
ei
g
h
t
co
r
r
elatio
n
is
p
r
o
p
o
s
ed
to
o
v
er
co
m
e
t
h
e
p
r
o
b
le
m
t
h
at
ad
d
r
ess
i
n
Sectio
n
1
.
B
e
f
o
r
e
p
r
o
ce
ed
in
g
,
th
e
o
r
i
g
in
al
d
ata
m
atr
i
x
i
s
s
ta
n
d
ar
d
ized
b
y
a
r
o
b
u
s
t
lo
ca
tio
n
an
d
s
ca
le
est
i
m
a
to
r
to
av
o
id
an
y
m
as
k
i
n
g
o
r
s
w
a
m
p
in
g
e
f
f
ec
t
[
1
3
]
.
T
h
e
r
ed
u
ce
d
d
ata
s
et
is
th
e
n
ap
p
lied
t
o
K
-
Me
a
n
s
cl
u
s
ter
an
al
y
s
is
to
o
b
tain
clu
s
ter
p
ar
titi
o
n
s
.
K
m
ea
n
s
m
et
h
o
d
r
eq
u
ir
es
s
p
ec
if
y
in
g
th
e
n
u
m
b
er
o
f
clu
s
ter
s
b
ef
o
r
e
th
e
alg
o
r
ith
m
is
ap
p
lied
.
T
o
o
v
er
co
m
e
t
h
is
p
r
o
b
lem
,
C
ali
n
s
k
i
an
d
Har
ab
asz
I
n
d
e
x
[
1
4
]
is
u
s
ed
as
a
m
ea
s
u
r
e
to
d
eter
m
in
e
t
h
e
o
p
ti
m
al
n
u
m
b
er
o
f
clu
s
ter
p
ar
titi
o
n
f
o
r
th
e
in
p
u
t
d
ata.
T
h
is
is
in
d
icate
d
b
y
th
e
m
a
x
i
m
u
m
v
al
u
e
o
f
th
e
i
n
d
ex
.
T
h
e
s
tep
s
in
v
o
l
v
ed
in
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
ar
e
as
f
o
llo
w
s
[
1
5
]
:
Step
1
: O
b
tain
th
e
i
n
p
u
t
m
atr
i
x
.
Step
2
: Stan
d
ar
d
ize
th
e
o
b
s
er
v
atio
n
w
it
h
m
ed
ian
a
n
d
m
ea
n
ab
s
o
lu
t
e
d
ev
iatio
n
(
M
A
D)
,
i.e
.
̅
(
|
(
)
|
)
s
u
c
h
th
a
t r
ef
er
to
ele
m
e
n
ts
i
n
t
h
e
in
p
u
t
m
a
tr
ix
.
Step
3
: Set
th
e
b
r
ea
k
d
o
w
n
p
o
in
t
f
o
r
th
e
T
u
k
e
y
's b
i
w
ei
g
h
t c
o
r
r
elatio
n
at
0
.
4
Step
4
: Calcu
late
th
e
T
u
k
e
y
's b
i
w
e
ig
h
t c
o
r
r
elatio
n
m
atr
i
x
.
Step
5
: Calcu
late
th
e
ei
g
e
n
v
ec
to
r
s
a
n
d
eig
en
v
alu
e
s
o
f
t
h
e
co
r
r
elatio
n
m
atr
i
x
.
Step
6
: Sele
ct
th
e
m
o
s
t
i
m
p
o
r
tan
t p
r
in
cip
al
co
m
p
o
n
e
n
ts
b
ased
o
n
c
u
m
u
lat
iv
e
p
er
ce
n
ta
g
e
o
f
to
tal
v
ar
iatio
n
.
Step
7
: D
er
iv
e
th
e
n
e
w
d
ata
s
et
Step
8
: Calcu
late
C
ali
n
s
k
i a
n
d
Har
ab
asz
in
d
ex
i
n
n
e
w
d
ata
s
et
to
d
eter
m
i
n
e
th
e
b
est
n
u
m
b
er
o
f
cl
u
s
ter
Step
9
: A
p
p
l
y
k
-
m
ea
n
s
m
et
h
o
d
to
n
ew
d
ata
s
et
3
.
3
.
Da
t
a
m
o
de
l o
f
ra
infa
ll f
o
r
t
he
s
i
m
ula
t
io
n pro
ce
du
re
Data
s
ets
ar
e
g
en
er
ated
b
ased
o
n
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
s
t
h
at
m
i
m
ic
a
m
u
lti
v
ar
iate
to
r
r
en
tial
r
ain
f
all
d
ata.
T
h
e
d
is
tr
ib
u
tio
n
s
o
f
tr
o
p
ical
r
ain
f
a
ll
d
ata
ar
e
g
e
n
er
al
l
y
s
k
e
w
ed
to
t
h
e
r
ig
h
t
a
n
d
t
h
u
s
d
is
tr
ib
u
tio
n
s
t
h
at
ex
h
ib
it
t
h
is
c
h
ar
ac
ter
is
tic
ca
n
b
e
u
s
ed
to
m
o
d
el
th
e
to
r
r
en
tial
r
ain
f
all.
T
h
r
ee
d
is
tr
ib
u
tio
n
s
ar
e
ch
o
s
en
w
h
ic
h
ar
e
g
a
m
m
a,
L
o
g
-
No
r
m
al
a
n
d
Gen
er
alize
d
P
ar
eto
d
is
tr
ib
u
tio
n
(
GP
D)
ar
e
test
ed
o
n
m
u
lti
v
ar
iate
r
ain
f
a
ll
d
ata.
T
h
ese
d
is
tr
ib
u
tio
n
s
ar
e
co
m
m
o
n
l
y
u
s
ed
as
p
o
ten
tial
ca
n
d
id
ates
f
o
r
th
e
d
ata
g
en
er
ati
n
g
m
e
ch
an
i
s
m
o
f
r
ain
f
all
d
ata
[
1
6
]
,
[
1
7
]
.
E
s
tim
at
io
n
o
f
t
h
e
p
ar
a
m
eter
s
f
o
r
ea
ch
o
f
t
h
e
ab
o
v
e
s
a
m
p
led
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
s
ar
e
b
ased
o
n
th
e
s
u
m
m
ar
y
s
tati
s
tics
f
r
o
m
t
h
e
o
r
ig
i
n
al
to
r
r
en
tial
r
ai
n
f
all
d
ata
in
P
en
i
n
s
u
lar
Ma
la
y
s
i
a.
T
h
ese
p
ar
am
eter
s
ar
e
b
ased
o
n
th
e
m
ea
n
a
n
d
s
ta
n
d
ar
d
d
ev
iatio
n
f
r
o
m
th
e
d
ata
s
et
o
f
2
5
0
to
r
r
en
tial
r
ai
n
f
a
ll
d
a
y
s
a
n
d
1
5
r
ain
f
all
s
tatio
n
s
o
f
t
h
e
o
r
ig
i
n
al
to
r
r
en
tial
r
ai
n
f
a
ll
d
ata
f
o
r
th
e
3
3
y
ea
r
p
er
io
d
in
P
en
in
s
u
l
ar
as
d
escr
ib
ed
in
Sectio
n
2
.
T
h
e
s
h
ap
e
p
ar
am
et
er
f
o
r
th
is
s
tu
d
y
is
ξ=0
.
2
w
h
e
r
e
it
p
e
r
f
o
r
m
s
w
el
l
f
o
r
an
d
v
er
y
g
o
o
d
f
o
r
[
1
8
]
.
Ou
t
o
f
t
h
e
th
r
ee
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
s
w
h
ich
w
er
e
s
a
m
p
le
d
,
GP
D
a
p
p
ea
r
s
to
f
it
th
e
d
ata
s
et
b
ased
o
n
s
ev
er
al
a
s
s
ess
m
e
n
t
s
b
y
d
is
tr
ib
u
tio
n
g
r
ap
h
s
(
i.e
QQ
p
lo
t
an
d
p
r
o
b
a
b
ilit
y
d
if
f
er
en
t
g
r
ap
h
)
an
d
g
o
o
d
n
ess
o
f
f
it
te
s
ts
u
s
i
n
g
C
h
i
-
s
q
u
ar
e
an
d
An
d
er
s
o
n
Dar
li
n
g
test
.
T
h
is
d
is
tr
ib
u
tio
n
i
s
r
e
m
ar
k
ab
l
y
g
o
o
d
at
s
ig
n
i
f
ica
n
ce
lev
el
if
,
th
u
s
p
r
o
v
id
in
g
s
o
m
e
ev
id
en
ce
th
at
t
h
e
n
u
ll
h
y
p
o
th
esi
s
is
tr
u
e
(
i.e
th
e
GP
D
p
r
o
v
id
es th
e
co
r
r
ec
t statis
tical
m
o
d
el
f
o
r
r
ain
f
a
ll d
ata)
.
Si
m
u
latio
n
s
w
er
e
ca
r
r
ied
o
u
t
o
n
s
a
m
p
le
GP
D
d
is
tr
ib
u
ti
o
n
s
ch
ar
ac
ter
ized
b
y
t
h
r
ee
p
ar
am
eter
s
lo
ca
tio
n
μ
=1
0
4
.
8
,
s
ca
le
σ
=5
4
.
7
an
d
s
h
ap
e
ξ=0
.
2
,
o
b
tain
ed
f
r
o
m
t
h
e
o
r
ig
i
n
al
to
r
r
en
tial
r
ain
f
all
d
ata
o
f
3
3
y
ea
r
p
er
io
d
in
P
en
in
s
u
lar
Ma
la
y
s
i
a
to
co
n
s
tr
u
ct
an
n
x
p
m
atr
i
x
w
ith
an
d
to
r
ep
r
esen
t
2
5
0
to
r
r
en
tial
r
ain
f
al
l
d
ay
s
an
d
1
5
r
ain
f
all
s
tatio
n
s
r
esp
ec
tiv
el
y
a
s
d
escr
ib
ed
in
Sectio
n
2
.
I
n
o
r
d
er
to
v
ar
y
th
e
s
i
m
u
lat
io
n
test
ed
,
t
w
o
d
if
f
er
e
n
t
s
etti
n
g
s
ar
e
u
s
ed
.
Firstl
y
,
t
h
e
s
ca
le
(
i.e
.
s
tan
d
ar
d
d
ev
iatio
n
)
ar
e
v
ar
ied
b
elo
w
an
d
ab
o
v
e
s
tan
d
ar
d
d
ev
iatio
n
o
f
th
e
o
r
ig
i
n
al
to
r
r
en
tial
r
ai
n
f
all
d
ata
to
ass
ess
t
h
e
ef
f
ec
t
o
f
p
r
eser
v
in
g
m
o
s
t
o
f
th
e
v
ar
i
atio
n
s
i
n
t
h
e
d
ata.
A
ll
g
en
er
ated
d
ata
clea
r
l
y
co
n
ta
in
s
v
al
u
e
s
o
f
ar
o
u
n
d
6
0
w
h
ic
h
r
ef
lect
t
h
e
6
0
m
m
/d
a
y
th
r
es
h
o
ld
o
f
to
r
r
en
tial
r
ain
f
all.
Seco
n
d
l
y
,
s
ev
er
al
r
an
g
e
o
f
b
r
ea
k
d
o
w
n
p
o
in
ts
b
et
w
ee
n
0
.
2
to
0
.
8
ar
e
test
ed
to
ev
alu
a
te
th
e
i
n
f
lu
e
n
ce
s
e
lectio
n
o
f
t
h
e
s
i
g
n
i
f
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s
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s
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s
ca
le
i
n
t
h
e
clu
s
ter
s
.
RE
F
E
R
E
NC
E
S
[1
]
M
o
ro
n
V
,
Ro
b
e
rtso
n
A
W
,
Qia
n
J
H,
G
h
il
M
.
W
e
a
th
e
r
Ty
p
e
s
Ac
ro
s
s
th
e
M
a
rit
im
e
Co
n
ti
n
e
n
t:
f
ro
m
th
e
Diu
rn
a
l
Cy
c
l
e
to
In
tera
n
n
u
a
l
V
a
riati
o
n
s.
Fro
n
ti
e
rs
in
En
v
ir
o
n
me
n
t
a
l
S
c
ien
c
e
.
2
0
1
5
;
3
(
4
4
)
:
1
-
1
9
.
[2
]
A
h
m
a
d
N
H,
Oth
m
a
n
IR,
De
n
i
S
M
.
Hie
ra
rc
h
ica
l
Clu
ste
r
A
p
p
ro
a
c
h
f
o
r
Re
g
io
n
a
li
z
a
ti
o
n
o
f
P
e
n
in
s
u
l
a
r
M
a
la
y
si
a
b
a
se
d
o
n
t
h
e
P
re
c
ip
i
tatio
n
Am
o
u
n
t.
J
o
u
rn
a
l
o
f
P
h
y
sic
s: Co
n
fer
e
n
c
e
S
e
rie
s
.
4
2
3
.
2
0
1
3
;
1
-
1
0
.
[3
]
S
iv
a
G
S
,
Ra
o
V
S
,
Ba
b
u
DR.
Clu
ste
r
A
n
a
l
y
sis
A
p
p
ro
a
c
h
to
S
tu
d
y
th
e
Ra
in
f
a
ll
P
a
t
tern
in
Visa
k
h
a
p
a
tn
a
m
District.
W
e
e
k
ly S
c
ien
c
e
Res
e
a
rc
h
J
o
u
rn
a
l
.
2
0
1
4
;1
(
3
1
).
[4
]
S
h
a
h
a
ru
d
in
S
M
,
A
h
m
a
d
N,
Yu
so
f
F
,
Ya
p
X
Q.
T
h
e
Co
m
p
a
riso
n
o
f
T
-
m
o
d
e
a
n
d
P
e
a
rso
n
Co
rre
la
ti
o
n
M
a
tri
c
e
s
in
Clas
sif
ic
a
ti
o
n
o
f
Da
il
y
Ra
in
fa
ll
P
a
tt
e
rn
s in
P
e
n
in
s
u
lar M
a
lay
sia
.
M
a
tem
a
ti
k
a
.
2
0
1
3
;
2
9
(
1
c
),
1
8
7
-
1
9
4
.
[5
]
Am
iri
M
A
,
M
e
s
g
a
ri
M
S
.
M
o
d
e
li
n
g
th
e
S
p
a
ti
a
l
a
n
d
T
e
m
p
o
ra
l
V
a
riab
il
i
ty
o
f
P
re
c
ip
it
a
ti
o
n
i
n
No
rth
w
e
st
Ira
n
.
A
t
m
o
sp
h
e
re
.
2
0
1
7
;
8
(2
5
4
)
,
1
-
1
4
.
[6
]
Ne
e
ti
N.
Ex
ten
d
in
g
T
-
M
o
d
e
Ca
n
o
n
ica
l
Co
rre
lati
o
n
A
n
a
ly
sis
to
T
-
M
o
d
e
P
re
-
F
il
tere
d
Ca
n
o
n
ica
l
Co
rre
latio
n
A
n
a
l
y
si
s:
A
No
v
e
l
A
p
p
ro
a
c
h
T
o
Disc
o
v
e
r
S
h
a
re
d
P
a
tt
e
rn
s
Be
twe
e
n
Tw
o
I
m
a
g
e
T
i
m
e
S
e
ries
.
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Rem
o
te
S
e
n
sin
g
.
2
0
1
4
,
3
5
(4
)
,
1
9
2
6
-
1
9
3
5
.
[7
]
Ra
y
m
a
e
k
e
rs,
J,
Ro
u
ss
e
e
u
w
P
J.
Fa
st
R
o
b
u
st
Co
rr
e
la
t
io
n
f
o
r
Hig
h
Dime
n
sio
n
a
l
Da
t
a
.
De
p
t
.
o
f
M
a
th
e
m
a
ti
c
s,
KU
L
e
u
v
e
n
,
Be
lg
iu
m
.
a
rX
iv
:1
7
1
2
.
0
5
1
5
1
.
2
0
1
8
.
[8
]
Ow
e
n
M
.
T
u
k
e
y
'
s Biwe
ig
h
t
Co
rre
latio
n
a
n
d
t
h
e
Bre
a
k
d
o
w
n
.
M
a
ste
r
T
h
e
sis.
Ca
li
f
o
rn
ia:
P
o
m
o
n
a
Co
ll
e
g
e
;
2
0
1
0
.
[9
]
S
a
h
a
k
R,
M
a
n
so
r
W
,
L
e
e
KY
,
Zab
id
i
A
.
P
e
rf
o
rm
a
n
c
e
o
f
P
ri
n
c
ip
a
l
Co
m
p
o
n
e
n
t
A
n
a
ly
sis
a
n
d
Orth
o
g
o
n
a
l
L
e
a
st
S
q
u
a
re
o
n
o
p
t
im
ize
d
F
e
a
tu
re
S
e
t
in
Clas
sify
in
g
A
sp
h
y
x
iate
d
In
f
a
n
t
Cry
u
sin
g
S
u
p
p
o
rt
V
e
c
to
r
M
a
c
h
in
e
.
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
a
n
d
Co
m
p
u
ter
S
c
ien
c
e
.
2
0
1
8
;
9
(
1
),
1
3
9
-
1
4
5
.
[1
0
]
Ru
il
ian
W
,
S
h
e
n
g
ji
a
n
G
.
Co
m
p
re
h
e
n
siv
e
Ev
a
lu
a
ti
o
n
to
Distrib
u
t
io
n
Ne
tw
o
rk
P
la
n
n
i
n
g
S
c
h
e
m
e
s
u
sin
g
P
ri
n
c
ip
a
l
Co
m
p
o
n
e
n
t
A
n
a
l
y
sis
M
e
th
o
d
.
TE
L
KO
M
NIK
A
In
d
o
n
e
sia
n
Jo
u
rn
a
l
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
.
2
0
1
4
;
1
2
(8
),
5
8
9
7
-
5
9
0
4
.
[1
1
]
Ne
w
a
re
S
,
M
e
h
ta
K,
Zad
g
a
o
n
k
a
r
A
S
.
F
in
g
e
r
Kn
u
c
k
le
Id
e
n
ti
f
ic
a
ti
o
n
u
si
n
g
P
ri
n
c
ip
a
l
Co
m
p
o
n
e
n
t
A
n
a
l
y
sis
a
n
d
Ne
a
re
st M
e
a
n
Clas
si
f
ier
.
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
Co
m
p
u
ter
A
p
p
l
ica
ti
o
n
s
.
2
0
1
3
;
7
0
(9
):
1
8
-
2
3
.
[1
2
]
S
h
a
h
a
ru
d
in
S
M
,
A
h
m
a
d
N.
M
o
d
e
li
n
g
,
De
sig
n
a
n
d
S
im
u
latio
n
S
y
ste
m
s.
In
:
A
li
M
S
M
,
S
a
h
la
n
S
,
W
a
h
id
H,
Yu
n
u
s
M
A
M
,
S
u
b
h
a
NA
M
a
n
d
W
a
h
a
p
A
R.
7
5
2
.
S
in
g
a
p
o
re
:
S
p
r
in
g
e
r;
2
0
1
7
:
2
1
6
-
2
2
4
.
[1
3
]
Ch
o
u
lak
ian
V
.
Ro
b
u
st
Q
-
M
o
d
e
P
ri
n
c
ip
a
l
Co
m
p
o
n
e
n
t
A
n
a
l
y
sis
i
n
L
1
.
Co
mp
u
t
a
ti
o
n
a
l
S
t
a
ti
stics
&
Da
ta
An
a
lys
i
s
.
2
0
0
1
;
3
7
:
1
3
5
-
1
5
0
.
[1
4
]
M
a
u
li
k
U.
2
0
0
2
.
P
e
rf
o
rm
a
n
c
e
Ev
a
lu
a
ti
o
n
o
f
S
o
m
e
Clu
ste
rin
g
A
lg
o
r
it
h
m
s
a
n
d
V
a
li
d
it
y
In
d
ice
s.
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
f
Pa
tt
e
rn
A
n
a
lys
is
a
n
d
M
a
c
h
i
n
e
In
t
e
ll
ig
e
n
c
e
.
2
0
0
2
;
2
4
(
1
2
):
1
6
5
0
-
1
6
5
4
.
[1
5
]
S
h
a
h
a
ru
d
in
S
M
,
A
h
m
a
d
N,
Y
u
so
f
F
.
I
m
p
ro
v
e
d
Clu
ste
r
P
a
rti
ti
o
n
i
n
P
rin
c
i
p
a
l
Co
m
p
o
n
e
n
t
A
n
a
l
y
si
s
G
u
id
e
d
Clu
ste
rin
g
.
I
n
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
Co
m
p
u
ter
A
p
p
l
ica
ti
o
n
s
.
2
0
1
3
;
7
5
(
1
1
):
2
2
-
2
5
.
[1
6
]
Ch
o
H
-
K,
B
o
wm
a
n
K.P
,
N
o
rth
G
.
R.
A
Co
m
p
a
riso
n
o
f
G
a
m
m
a
a
n
d
L
o
g
n
o
rm
a
l
Distrib
u
ti
o
n
f
o
r
Ch
a
ra
c
teriz
in
g
S
a
telli
te
Ra
in
Ra
tes
f
ro
m
th
e
T
r
o
p
ica
l
Ra
in
f
a
ll
M
e
a
su
rin
g
M
issio
n
.
J
o
u
rn
a
l
o
f
A
p
p
li
e
d
M
e
teo
ro
l
o
g
y
.
2
0
0
4
;
4
3
:
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