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a
k
e
o
cc
u
r
r
en
ce
b
y
tak
in
g
r
ad
o
n
g
as
co
n
ce
n
tr
atio
n
d
ata
f
o
r
th
e
n
e
x
t
f
ew
d
ay
s
as
in
p
u
t
[
3
]
,
[
1
0
]
,
[
1
7
]
–
[
2
0
]
.
Ho
wev
er
,
f
ew
s
tu
d
ies
ca
n
s
till
p
r
ed
ict
ea
r
th
q
u
ak
es'
lo
ca
tio
n
b
ased
o
n
r
ad
o
n
g
as
p
r
ec
u
r
s
o
r
s
.
Sev
er
al
s
tu
d
ies
h
av
e
tr
ie
d
to
p
r
ed
ict
t
h
e
lo
ca
tio
n
o
f
ea
r
th
q
u
ak
es
b
y
u
tili
zin
g
h
is
to
r
ical
d
ata
o
n
ea
r
t
h
q
u
ak
e
ev
en
ts
,
tak
in
g
d
ep
t
h
a
n
d
m
ag
n
itu
d
e
as th
e
m
ai
n
f
ea
t
u
r
es
[
2
1
]
.
R
esear
ch
b
y
Pra
tam
a
et
a
l.
[
3
]
,
o
n
e
o
f
th
e
ea
r
t
h
q
u
a
k
e
ea
r
ly
war
n
in
g
s
y
s
tem
r
esear
ch
team
s
h
as
d
ev
elo
p
e
d
a
s
tatis
tical
m
eth
o
d
f
o
r
p
r
e
d
ictin
g
t
h
e
tim
e
a
n
ea
r
th
q
u
ak
e
will
o
cc
u
r
.
T
h
is
m
eth
o
d
ca
n
p
r
o
d
u
ce
an
ac
cu
r
ac
y
o
f
7
5
%
in
s
ettin
g
an
ea
r
th
q
u
ak
e
ala
r
m
1
to
4
d
ay
s
af
ter
th
e
alar
m
is
ac
tiv
e
[
2
]
.
I
n
th
is
r
esear
ch
,
th
e
m
ain
o
b
jectiv
e
is
to
co
m
p
lem
en
t
th
e
Pra
tam
a
et
a
l.
a
p
p
r
o
ac
h
b
y
p
r
ed
ictin
g
th
e
lo
ca
ti
o
n
o
f
t
h
e
ea
r
t
h
q
u
a
k
e
ep
icen
ter
[
3
]
.
Pre
d
ictin
g
th
e
lo
ca
tio
n
o
f
th
e
ep
icen
ter
h
as
a
ce
n
tr
al
r
o
le
in
ea
r
ly
war
n
i
n
g
o
f
ea
r
t
h
q
u
a
k
es,
esp
ec
ially
in
ea
r
th
q
u
ak
e
-
p
r
o
n
e
ar
ea
s
s
u
ch
as
th
e
m
ee
tin
g
o
f
th
e
I
n
d
o
-
Au
s
tr
alian
an
d
E
u
r
asian
p
lates.
R
ad
o
n
g
as
co
n
ce
n
t
r
atio
n
d
ata
a
n
d
m
ac
h
in
e
lear
n
in
g
m
eth
o
d
s
ar
e
cr
itical
elem
en
ts
in
p
r
ed
ictin
g
th
e
lo
ca
tio
n
o
f
t
h
e
ea
r
th
q
u
ak
e
ep
icen
ter
p
r
o
p
o
s
ed
in
th
is
r
esear
ch
.
Ho
p
ef
u
lly
,
th
is
r
esear
ch
ca
n
co
n
tr
ib
u
te
to
r
ed
u
cin
g
th
e
im
p
ac
t
o
f
n
atu
r
al
d
is
aster
s
ca
u
s
ed
b
y
ea
r
th
q
u
ak
es.
2.
RE
S
E
ARCH
M
E
T
H
O
D
I
n
th
e
m
eth
o
d
s
ec
tio
n
,
th
is
r
esear
ch
will
o
u
tlin
e
th
e
b
asi
c
co
n
ce
p
ts
o
f
th
r
ee
en
s
em
b
le
lear
n
in
g
tech
n
iq
u
es
ess
en
tial
in
m
ac
h
in
e
lear
n
in
g
:
g
r
ad
ien
t
b
o
o
s
tin
g
,
Ad
aBo
o
s
t
,
an
d
r
an
d
o
m
f
o
r
e
s
t
.
T
h
e
s
eq
u
e
n
ce
o
f
th
is
r
esear
ch
is
d
ata
co
llectio
n
,
d
ataset
p
r
e
-
p
r
o
ce
s
s
in
g
,
m
ac
h
in
e
lear
n
in
g
m
o
d
elin
g
,
an
d
m
o
d
el
s
elec
tio
n
b
ased
o
n
th
e
b
est
r
o
o
t
m
ea
n
s
q
u
ar
e
er
r
o
r
(
R
MSE
)
.
Dis
tan
ce
r
esu
lt
s
f
r
o
m
t
h
e
m
o
d
el
with
th
e
lo
west
R
MSE
wil
l
b
e
u
s
ed
as th
e
p
r
ed
ictio
n
.
T
h
e
r
esear
c
h
m
eth
o
d
d
iag
r
am
ca
n
b
e
s
ee
n
in
Fig
u
r
e
1
.
Fig
u
r
e
1
.
B
lo
ck
d
iag
r
am
o
f
r
es
ea
r
ch
m
eth
o
d
s
2
.
1
.
Da
t
a
c
o
llect
io
n
T
h
e
r
esear
ch
em
p
lo
y
ed
a
c
o
m
p
r
eh
e
n
s
iv
e
d
ata
co
llectio
n
s
tr
ateg
y
in
co
r
p
o
r
atin
g
p
r
i
m
ar
y
an
d
s
ec
o
n
d
ar
y
s
o
u
r
ce
s
.
Prim
ar
y
d
ata,
cr
u
cial
to
th
e
s
tu
d
y
,
c
o
n
s
is
ted
o
f
d
aily
av
er
ag
e
r
a
d
o
n
g
as
m
ea
s
u
r
em
en
ts
o
b
tain
ed
f
r
o
m
s
ix
telem
o
n
ito
r
in
g
s
tatio
n
s
:
Pacitan
,
B
an
tu
l
,
Pr
am
b
an
an
,
Ma
g
u
wo
,
Ser
a
n
g
,
an
d
B
ali.
T
h
is
d
ataset
co
m
p
r
is
ed
1
4
d
ata
p
o
i
n
ts
,
s
p
ec
if
ically
th
e
d
aily
av
er
ag
e
r
ad
o
n
g
as
co
n
ce
n
tr
atio
n
(
B
q
/m
3
)
,
o
f
f
er
in
g
a
r
o
b
u
s
t
d
e
p
ictio
n
o
f
th
e
v
ar
ian
ce
s
in
r
ad
o
n
g
as
le
v
els
th
r
o
u
g
h
o
u
t
th
e
d
esig
n
ate
d
p
e
r
io
d
.
C
o
m
p
lem
en
tin
g
t
h
e
p
r
im
ar
y
d
ata,
s
ec
o
n
d
ar
y
d
ata
was
s
o
u
r
ce
d
f
r
o
m
th
e
Po
ts
d
am
Geo
f
o
n
s
ite,
p
r
o
v
i
d
in
g
es
s
en
tial
in
f
o
r
m
atio
n
ab
o
u
t
th
e
co
o
r
d
in
ates
o
f
ea
r
th
q
u
ak
e
ep
ice
n
ter
s
.
T
h
ese
co
o
r
d
in
ates
u
n
d
er
wen
t
co
n
v
er
s
io
n
u
s
in
g
th
e
Hav
er
s
in
e
f
o
r
m
u
la
t
o
d
er
i
v
e
d
is
tan
ce
v
alu
es
b
etwe
en
th
e
e
ar
th
q
u
ak
e
ep
icen
ter
an
d
ea
ch
telem
o
n
i
to
r
in
g
s
tatio
n
.
T
h
e
co
m
b
in
atio
n
o
f
p
r
im
ar
y
a
n
d
s
ec
o
n
d
ar
y
d
ata
is
th
e
b
asic
tr
ai
n
in
g
m
ater
ial
f
o
r
th
e
s
u
p
e
r
v
is
ed
m
ac
h
in
e
lear
n
in
g
p
r
o
ce
s
s
.
Th
is
m
ac
h
in
e
lear
n
in
g
ap
p
r
o
ac
h
aim
e
d
to
p
r
e
d
ict
th
e
d
is
tan
ce
f
r
o
m
ea
c
h
telem
o
n
it
o
r
in
g
s
tatio
n
to
t
h
e
im
p
en
d
in
g
ea
r
t
h
q
u
a
k
e
ep
ice
n
ter
,
e
n
h
an
cin
g
th
e
p
r
ec
is
io
n
an
d
ef
f
icac
y
o
f
ea
r
th
q
u
ak
e
ep
icen
ter
lo
ca
tio
n
f
o
r
ec
asti
n
g
.
T
h
e
2
2
4
d
ata
co
l
lectio
n
p
er
i
o
d
f
o
r
r
ad
o
n
g
as
an
d
ea
r
th
q
u
a
k
es
s
tar
ts
f
r
o
m
J
an
u
ar
y
2
0
,
2
0
2
2
to
Ap
r
il 3
0
,
2
0
2
3
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
E
a
r
th
q
u
a
ke
e
p
icen
ter p
r
ed
ictio
n
fr
o
m
th
e
Ja
v
a
B
a
li ra
d
o
n
g
a
s
…
(
C
h
r
is
to
p
h
o
r
u
s
A
r
g
a
P
u
t
r
a
n
to
)
41
2
.
2
.
Da
t
a
p
r
e
-
pro
ce
s
s
ing
Data
p
r
e
-
p
r
o
ce
s
s
in
g
f
o
r
p
r
im
ar
y
an
d
s
ec
o
n
d
ar
y
d
ata
is
ca
r
r
ied
o
u
t
b
ef
o
r
e
en
ter
in
g
th
e
m
ac
h
in
e
lear
n
in
g
p
r
o
ce
s
s
.
T
h
e
d
ata
w
ill
b
e
clea
n
ed
f
r
o
m
n
u
ll,
em
p
ty
d
ata,
m
is
s
in
g
v
alu
es,
an
d
o
u
tlier
s
th
at
ca
n
in
f
lu
en
ce
p
r
e
d
ictio
n
r
esu
lts
.
R
em
o
v
in
g
o
u
tlier
s
wh
er
e
d
ata
p
o
in
ts
f
ar
f
r
o
m
m
o
s
t
o
th
er
d
ata
ar
e
id
en
tifie
d
an
d
r
em
o
v
ed
.
Ou
tlier
s
wer
e
r
em
o
v
ed
b
ased
o
n
t
h
e
z
-
s
co
r
e
v
al
u
e,
with
5
%
o
f
th
e
z
-
s
co
r
e
a
s
o
u
tlier
s
f
r
o
m
t
h
e
en
tire
av
er
a
g
e
r
a
d
o
n
g
as
co
n
c
en
tr
atio
n
d
ata
p
er
telem
etr
y
s
t
atio
n
.
T
h
e
r
ef
o
r
e
,
a
9
5
%
z
-
s
co
r
e
o
f
th
e
wh
o
le
d
ata
f
r
o
m
ea
ch
s
tatio
n
will
b
e
u
s
e
d
.
T
h
e
d
ata
p
r
ep
r
o
c
ess
in
g
p
r
o
ce
s
s
r
esu
lted
in
f
ea
tu
r
es
in
th
e
f
o
r
m
o
f
a
clea
n
d
ataset.
T
h
e
d
ataset
is
d
iv
id
e
d
in
to
two
p
ar
ts
:
f
ea
tu
r
es
(
X)
,
th
e
p
r
im
ar
y
d
ata
v
a
r
iab
les
u
s
ed
as
f
ea
tu
r
es
to
p
r
ed
ict
th
e
tar
g
et
v
ar
iab
le,
an
d
tar
g
et
(
y
)
,
wh
ich
is
th
e
d
is
tan
ce
o
f
th
e
ta
r
g
et
to
p
r
e
d
ict.
T
h
e
d
ataset
is
f
u
r
th
er
d
iv
id
ed
in
t
o
two
s
u
b
s
ets:
a
tr
ain
in
g
s
et
an
d
a
test
in
g
s
et.
T
h
e
tr
ain
in
g
s
et
(
8
0
%)
wh
ile
th
e
test
in
g
s
et
(
2
0
%).
T
h
is
s
p
lit
en
s
u
r
es
th
at
t
h
e
m
o
d
el
is
tr
ain
ed
o
n
o
n
e
d
ata
s
et
an
d
test
ed
o
n
an
o
th
e
r
s
et
it
h
as
n
o
t
s
ee
n
b
ef
o
r
e.
T
h
e
m
ac
h
in
e
lear
n
in
g
m
o
d
e
lin
g
p
r
o
ce
s
s
ca
n
b
e
ca
r
r
ied
o
u
t
with
th
e
tr
ain
in
g
an
d
test
d
ata.
T
h
e
d
ata
p
r
o
ce
s
s
in
g
s
tep
s
ar
e
p
r
esen
ted
in
Fig
u
r
e
2
.
Fig
u
r
e
2
.
D
ata
ac
q
u
is
itio
n
an
d
p
r
e
-
p
r
o
ce
s
s
in
g
2
.
3
.
M
a
chine le
a
rning
T
h
e
m
ain
d
if
f
er
e
n
ce
b
etwe
en
g
r
ad
ien
t
b
o
o
s
tin
g
,
Ad
aBo
o
s
t
,
an
d
r
a
n
d
o
m
f
o
r
est
is
h
o
w
th
e
y
co
m
b
i
n
e
wea
k
m
o
d
els.
Gr
ad
ien
t
b
o
o
s
tin
g
f
o
cu
s
es
o
n
g
r
ad
u
ally
r
e
d
u
cin
g
p
r
ed
ictio
n
er
r
o
r
s
b
y
i
m
p
r
o
v
i
n
g
p
r
e
v
io
u
s
m
o
d
els,
Ad
aBo
o
s
t
g
iv
es
m
o
r
e
weig
h
t
to
m
is
class
if
ied
s
a
m
p
les,
wh
ile
r
an
d
o
m
f
o
r
est
co
m
b
in
es
p
r
ed
ictio
n
s
f
r
o
m
m
a
n
y
d
ec
is
io
n
tr
ee
s
in
p
ar
allel.
T
h
e
ap
p
r
o
p
r
iate
tec
h
n
iq
u
e
s
elec
tio
n
d
ep
e
n
d
s
o
n
th
e
p
r
o
b
lem
to
b
e
s
o
lv
ed
an
d
th
e
d
ata
ch
ar
ac
ter
is
tics
u
s
ed
.
I
n
th
is
r
esear
ch
,
we
will
co
m
p
ar
e
th
e
p
er
f
o
r
m
an
ce
o
f
th
ese
th
r
ee
tech
n
iq
u
es in
th
e
c
o
n
tex
t t
o
s
ee
th
e
s
tr
en
g
th
s
an
d
wea
k
n
ess
es o
f
ea
ch
m
o
d
el
[
1
6
]
,
[
2
2
]
–
[
2
6
]
.
2
.
4
.
M
et
ho
d im
plem
ent
a
t
io
n
2
.
4
.
1
.
Alg
o
rit
hm
t
ra
ini
ng
Af
ter
th
e
d
ata
p
r
e
-
p
r
o
ce
s
s
in
g
s
tag
e,
th
e
d
ata
will
b
e
s
ep
ar
ated
in
to
two
s
u
b
s
e
ts
,
n
am
ely
tr
ain
d
ata
an
d
v
alid
atio
n
d
ata,
th
r
o
u
g
h
t
h
e
d
ata
s
ep
ar
atio
n
s
tag
e.
T
r
ain
d
ata
is
u
s
ed
t
o
tr
ain
t
h
e
m
o
d
el,
wh
ile
v
alid
atio
n
d
ata
is
u
s
ed
to
m
ea
s
u
r
e
th
e
m
o
d
el'
s
p
er
f
o
r
m
an
ce
d
u
r
in
g
tr
ain
in
g
a
n
d
h
elp
in
p
ar
am
ete
r
tu
n
in
g
.
T
h
e
f
i
n
al
s
tag
e
in
th
e
tr
ain
in
g
p
r
o
ce
s
s
is
m
ac
h
in
e
lear
n
in
g
tr
ain
in
g
,
wh
er
e
t
h
e
s
elec
ted
alg
o
r
ith
m
will
b
e
ap
p
lied
to
th
e
tr
ain
d
ata
an
d
ad
ju
s
ted
to
t
h
e
p
at
ter
n
s
in
th
e
d
ata.
T
h
is
p
r
o
c
ess
will
b
e
r
ep
ea
ted
an
d
ad
j
u
s
ted
with
v
a
r
io
u
s
p
ar
am
eter
s
u
n
til
th
e
m
o
d
el
ac
h
iev
es
p
er
f
o
r
m
an
ce
th
at
m
ee
t
s
th
e
r
esear
ch
d
e
m
an
d
s
.
Go
i
n
g
th
r
o
u
g
h
th
is
s
er
ies
o
f
s
tag
es
ca
r
ef
u
lly
en
s
u
r
es
th
at
th
e
r
esu
ltin
g
m
ac
h
in
e
-
le
ar
n
in
g
m
o
d
el
ca
n
p
r
o
v
id
e
a
cc
u
r
ate
an
d
u
s
ef
u
l
p
r
ed
ictio
n
s
.
2
.
4
.
2
.
G
ra
dient
bo
o
s
t
ing
im
p
lem
ent
a
t
io
n
Gr
ad
ien
t
bo
o
s
tin
g
im
p
lem
en
t
atio
n
co
n
s
is
ts
o
f
s
ev
er
al
cr
u
cial
s
tag
es.
T
h
e
f
ir
s
t
s
tag
e
is
s
e
lectin
g
th
e
b
ase
m
o
d
el
to
b
e
u
s
ed
.
I
t
u
s
ed
a
d
ec
is
io
n
tr
ee
(
d
ec
is
io
n
tr
ee
s
)
as
a
b
ase
m
o
d
el
k
n
o
wn
as
g
r
ad
ien
t
b
o
o
s
tin
g
with
d
ec
is
io
n
tr
ee
s
o
r
g
r
a
d
ien
t
b
o
o
s
ted
tr
ee
s
(
GB
T
)
.
T
h
e
s
ec
o
n
d
s
tep
is
th
e
in
itializatio
n
o
f
th
e
GB
T
m
o
d
el.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
1
4
,
No
.
1
,
Ma
r
c
h
2
0
2
5
:
39
-
45
42
I
n
itially
,
th
is
m
o
d
el
will
h
av
e
eq
u
al
weig
h
ts
f
o
r
all
tr
ain
i
n
g
d
ata.
T
h
en
,
it
will
r
u
n
iter
atio
n
s
to
p
r
o
d
u
ce
s
ev
er
al
d
ec
is
io
n
tr
ee
s
.
E
ac
h
it
er
atio
n
tr
ain
s
a
d
ec
is
io
n
tr
ee
u
s
in
g
th
e
g
r
ad
ien
t
o
f
th
e
lo
s
s
f
u
n
ctio
n
ag
ai
n
s
t
th
e
p
r
ev
io
u
s
p
r
e
d
ictio
n
.
T
h
is
will
g
iv
e
g
r
ea
ter
weig
h
t
to
d
at
a
th
at
ea
r
lier
m
o
d
els
h
a
d
d
if
f
icu
lty
e
x
p
lain
in
g
.
Fu
r
th
er
m
o
r
e
,
ea
ch
n
ewly
ad
d
ed
d
ec
is
io
n
tr
ee
will
h
av
e
its
weig
h
t
in
th
e
en
s
em
b
le
m
o
d
el.
I
t
is
co
m
b
in
in
g
p
r
ed
ictio
n
s
f
r
o
m
all
d
ec
is
io
n
t
r
ee
s
p
o
s
s
ib
le
to
p
r
o
d
u
ce
a
f
in
a
l
p
r
ed
ictio
n
.
I
t
will
also
p
ay
at
ten
tio
n
to
ess
en
tial
p
ar
am
eter
s
s
u
ch
as lea
r
n
in
g
r
a
te
an
d
tr
ee
d
e
p
th
to
c
o
n
tr
o
l m
o
d
el
co
m
p
lex
ity
[
2
5
]
,
[
2
7
]
,
[
2
8
]
.
2
.
4
.
3
.
Ada
B
o
o
s
t
i
m
plem
ent
a
t
io
n
I
n
th
e
im
p
lem
e
n
tatio
n
p
h
ase
o
f
Ad
aBo
o
s
t
,
th
e
s
tep
s
in
im
p
lem
en
tin
g
th
is
tech
n
iq
u
e
ar
e
f
o
llo
wed
ca
r
ef
u
lly
.
T
h
e
wea
k
m
o
d
el
is
ch
o
s
en
as
th
e
b
ase
m
o
d
el,
w
ith
a
d
ec
is
io
n
tr
ee
t
h
at
h
as
li
m
ited
d
ep
t
h
as
th
e
ch
o
ice,
wh
ich
will
b
e
ad
ap
ted
ad
ap
tiv
ely
d
u
r
in
g
th
e
tr
ain
i
n
g
p
r
o
ce
s
s
.
Nex
t,
weig
h
t
in
itializat
io
n
is
ca
r
r
ied
o
u
t
f
o
r
ea
c
h
tr
ai
n
in
g
d
ata
s
am
p
le
,
em
p
h
asizin
g
m
is
class
if
ied
s
am
p
les
at
ea
ch
iter
atio
n
.
T
h
e
weig
h
ts
f
o
r
ea
c
h
s
am
p
le
ar
e
in
itially
s
et
u
n
if
o
r
m
ly
.
I
ter
atio
n
s
ar
e
ca
r
r
ied
o
u
t,
wh
er
e
th
e
b
ase
m
o
d
el
is
tr
ai
n
ed
o
n
t
h
e
tr
ain
i
n
g
d
ata
with
weig
h
ts
ad
ju
s
ted
ad
ap
tiv
ely
.
Sam
p
les
m
is
clas
s
if
ie
d
in
th
e
p
r
ev
io
u
s
iter
atio
n
will
b
e
g
iv
en
g
r
ea
ter
weig
h
t
in
th
e
n
ex
t
iter
atio
n
.
T
h
is
iter
atio
n
co
n
tin
u
es
u
n
til
th
e
s
p
ec
if
ied
n
u
m
b
er
o
f
iter
atio
n
s
is
r
ea
ch
ed
o
r
a
s
u
f
f
icien
t
ac
cu
r
ac
y
lev
el
is
ac
h
iev
ed
.
Fin
ally
,
p
r
ed
ict
io
n
s
f
r
o
m
all
b
ase
m
o
d
els
a
r
e
co
m
b
in
ed
u
s
in
g
weig
h
ts
ap
p
r
o
p
r
iate
to
ea
c
h
m
o
d
el.
T
h
e
f
in
al
r
esu
lt
o
f
th
is
e
n
s
em
b
le
is
an
Ad
aBo
o
s
t
m
o
d
el
th
at
h
as
b
ee
n
tr
ain
ed
t
o
p
r
ed
ict
th
e
d
is
tan
ce
to
th
e
ep
ic
en
ter
o
f
a
n
ea
r
th
q
u
ak
e
f
r
o
m
telem
o
n
ito
r
in
g
s
tatio
n
d
ata
[
2
2
]
,
[
2
9
]
,
[
3
0
]
.
2
.
4
.
4
.
Ra
nd
o
m
f
o
re
s
t
im
ple
m
ent
a
t
io
n
I
n
th
e
r
an
d
o
m
f
o
r
est
im
p
lem
en
tatio
n
p
h
ase,
th
e
s
tep
s
ar
e
ca
r
ef
u
lly
g
u
id
e
d
to
p
r
o
d
u
ce
a
r
eliab
le
en
s
em
b
le
m
o
d
el.
First,
ch
o
o
s
e
th
e
n
u
m
b
er
o
f
d
ec
is
io
n
tr
ee
s
th
at
will f
o
r
m
th
e
en
s
em
b
le
an
d
o
th
er
p
ar
am
eter
s
,
s
u
ch
as
th
e
n
u
m
b
e
r
o
f
r
an
d
o
m
f
ea
tu
r
es
u
s
ed
in
ea
ch
tr
ee
.
T
h
e
s
ec
o
n
d
s
te
p
is
to
cr
ea
te
a
r
an
d
o
m
d
ata
s
am
p
le
s
et
with
r
ep
lace
m
en
t
f
r
o
m
th
e
tr
ain
in
g
d
ataset
f
o
r
ea
c
h
tr
ee
.
T
h
is
en
s
u
r
es
v
ar
iatio
n
in
th
e
d
ata
u
s
ed
to
tr
ain
ea
ch
tr
ee
,
h
elp
in
g
to
av
o
id
o
v
er
f
itti
n
g
.
E
ac
h
d
ec
is
io
n
tr
e
e
is
tr
ain
ed
o
n
a
d
ata
s
et
th
at
h
as
b
ee
n
cr
ea
ted
.
Se
p
ar
atio
n
cr
iter
ia
s
u
ch
as
Gin
i
im
p
u
r
ity
o
r
e
n
tr
o
p
y
t
o
b
u
il
d
an
o
p
tim
al
d
ec
is
io
n
tr
ee
at
ea
ch
iter
atio
n
[
1
6
]
,
[
2
6
]
,
[
3
1
]
,
[
3
2
]
.
Du
r
in
g
test
in
g
,
it
u
s
es
v
alid
atio
n
d
ata
s
ets
to
m
ea
s
u
r
e
th
e
p
er
f
o
r
m
an
ce
o
f
ea
ch
tr
ee
s
ep
ar
ately
.
Fin
ally
,
th
e
p
r
ed
i
ctio
n
s
f
r
o
m
ea
ch
tr
ee
will
b
e
co
m
b
in
ed
v
ia
m
aj
o
r
ity
v
o
t
e
(
class
i
f
icatio
n
)
o
r
av
er
ag
e
(
r
eg
r
ess
io
n
)
to
p
r
o
d
u
c
e
a
f
in
al
en
s
em
b
le
p
r
ed
ictio
n
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
On
e
o
f
th
e
im
p
o
r
tan
t
m
etr
ics
u
s
ed
in
th
e
e
v
alu
atio
n
p
r
o
ce
s
s
is
R
M
SE.
Du
r
in
g
t
h
e
ev
al
u
atio
n
,
all
m
o
d
els
wer
e
tr
ain
ed
o
n
th
e
v
alid
atio
n
d
ataset
an
d
m
ea
s
u
r
ed
th
e
R
MSE
o
f
ea
ch
m
o
d
el.
To
ch
o
o
s
e
th
e
b
est
alg
o
r
ith
m
f
o
r
im
p
lem
en
tin
g
e
p
icen
ter
d
is
tan
ce
p
r
ed
ictio
n
,
th
e
m
o
d
el
with
th
e
s
m
allest
R
MSE
is
s
elec
ted
,
n
am
ely
t
h
e
m
o
d
el
with
th
e
h
i
g
h
est
ac
cu
r
ac
y
le
v
el
in
p
r
e
d
ic
tin
g
th
is
d
is
tan
ce
.
C
h
o
o
s
in
g
t
h
e
b
est
al
g
o
r
ith
m
is
es
s
en
tial
in
en
s
u
r
in
g
th
at
th
e
ep
icen
ter
d
is
tan
ce
p
r
ed
ictio
n
to
b
e
im
p
lem
en
ted
h
as a
h
ig
h
le
v
el
o
f
ac
cu
r
ac
y
an
d
is
r
eliab
le.
T
h
u
s
,
th
e
m
ac
h
i
n
e
lear
n
in
g
m
o
d
el
ev
alu
atio
n
r
es
u
lts
ar
e
th
e
b
asis
f
o
r
s
elec
tin
g
a
m
o
d
el
th
at
will b
e
u
s
ed
f
o
r
d
is
tan
ce
p
r
ed
ictio
n
in
th
e
co
n
tex
t
o
f
r
a
d
o
n
g
as tele
m
o
n
ito
r
in
g
s
tatio
n
s
.
Fo
llo
win
g
th
e
tr
ain
in
g
p
h
ase
u
tili
zin
g
g
r
ad
ien
t
b
o
o
s
tin
g
,
Ad
aBo
o
s
t
,
an
d
r
an
d
o
m
f
o
r
es
t
m
ac
h
in
e
lear
n
in
g
m
o
d
els,
th
e
d
ataset
w
as
s
p
lit
in
to
8
0
%
(
1
7
9
d
ata)
f
o
r
tr
ain
in
g
d
ata
an
d
2
0
%
(
4
5
d
a
ta)
f
o
r
test
in
g
d
ata
to
ass
es
s
th
e
m
o
d
els'
p
r
ed
icti
v
e
ca
p
ab
ilit
ies.
All
th
r
ee
m
o
d
els
d
em
o
n
s
tr
ated
p
r
o
f
icien
c
y
in
p
r
ed
ictin
g
th
e
d
is
tan
ce
b
etwe
en
t
h
e
ea
r
t
h
q
u
ak
e
ep
icen
te
r
an
d
th
e
telem
et
r
y
s
tatio
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.
T
h
e
p
r
ed
ictio
n
p
er
f
o
r
m
an
ce
in
d
icato
r
s
wer
e
ev
alu
ated
b
ased
o
n
th
e
o
u
tco
m
es
o
f
d
is
tan
ce
p
r
ed
i
ctio
n
test
s
co
n
d
u
cted
with
t
h
e
tr
ain
e
d
m
ac
h
in
e
lear
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in
g
m
o
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els.
T
h
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s
t
m
o
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el
co
n
s
is
ten
tly
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ield
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d
th
e
m
o
s
t
f
av
o
r
ab
le
R
MSE
r
esu
lts
,
s
ig
n
if
y
in
g
s
u
p
er
io
r
p
r
ed
i
ctiv
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ac
cu
r
ac
y
.
T
h
e
d
etailed
R
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alu
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f
o
r
ea
ch
r
ad
o
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g
as
telem
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n
ito
r
in
g
s
tatio
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n
d
e
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ea
c
h
m
ac
h
in
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-
lear
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i
n
g
alg
o
r
ith
m
ar
e
p
r
esen
ted
in
T
ab
le
1
.
Ad
d
iti
o
n
ally
,
th
e
c
o
r
r
esp
o
n
d
in
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R
M
SE
tr
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g
r
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is
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ally
r
ep
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Fig
u
r
e
3
,
p
r
o
v
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a
co
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p
r
eh
e
n
s
iv
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v
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v
iew
o
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th
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m
o
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el'
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m
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t
s
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in
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ig
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th
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m
ac
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ith
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s
in
p
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q
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is
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,
wh
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cr
u
cial
f
o
r
r
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in
g
an
d
o
p
tim
izin
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f
u
t
u
r
e
p
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e
d
ictiv
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m
o
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els.
B
ased
o
n
th
e
p
r
ed
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n
r
esu
lts
o
b
tain
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,
it
ca
n
b
e
s
ee
n
f
r
o
m
Fig
u
r
e
3
,
th
at
ea
ch
s
tatio
n
h
as
a
d
if
f
er
en
t
b
est
alg
o
r
ith
m
.
T
h
e
b
est
alg
o
r
ith
m
at
Pacitan
s
tatio
n
i
s
Ad
aBo
o
s
t
;
at
B
an
tu
l
s
tatio
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is
r
an
d
o
m
f
o
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est
;
at
Pra
m
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an
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tatio
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it
is
Ad
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t
; a
t M
ag
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d
o
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; a
t Ser
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g
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it
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I
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R
MSE
is
u
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in
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Fo
r
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t b
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o
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f
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n
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t b
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u
s
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as a
n
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o
lu
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f
er
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ce
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r
ith
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
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8
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E
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43
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r
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Gr
a
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h
o
f
R
MSE
p
r
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d
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s
f
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m
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c
h
s
tatio
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4.
CO
NCLU
SI
O
N
E
ar
th
q
u
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k
es
r
em
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m
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a
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s
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esp
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ex
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s
iv
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r
esear
ch
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f
o
r
ts
to
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n
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e
r
s
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d
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.
f
r
o
m
test
in
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alg
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r
ith
m
f
o
r
p
r
e
d
ictin
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is
tan
ce
o
f
th
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r
th
q
u
ak
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ep
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ter
f
r
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m
6
J
av
a
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ali
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g
as
telem
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tatio
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ly
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t
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m
f
o
r
est
)
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T
h
e
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o
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ith
m
v
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ac
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s
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im
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is
tics
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en
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g
p
r
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d
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m
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I
t
was
co
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cl
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d
ed
th
at
t
h
e
b
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r
ith
m
was
r
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m
f
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with
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f
4
5
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1
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k
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Ov
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f
in
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tr
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ts
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to
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th
q
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y
r
e
f
in
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p
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m
o
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els
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d
co
n
s
id
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in
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s
tatio
n
-
s
p
ec
if
ic
f
a
cto
r
s
,
s
u
ch
as
g
e
o
lo
g
ical
co
n
d
itio
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s
an
d
d
ata
v
ar
iab
ilit
y
,
r
esear
ch
er
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ca
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f
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th
e
r
ad
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r
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tem
s
an
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m
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th
e
im
p
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f
s
e
is
m
ic
ev
en
ts
o
n
v
u
l
n
er
ab
le
co
m
m
u
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ities
.
ACK
NO
WL
E
DG
E
M
E
NT
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T
h
an
k
y
o
u
to
th
e
Sen
s
o
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tem
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T
ele
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o
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ab
o
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at
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r
y
r
esear
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at
th
e
Dep
ar
tm
en
t
o
f
Nu
clea
r
E
n
g
in
ee
r
i
n
g
an
d
Ph
y
s
ical
E
n
g
in
ee
r
in
g
,
Un
iv
e
r
s
itas
Gad
jah
Ma
d
a
,
I
n
d
o
n
esia,
f
o
r
p
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o
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g
a
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g
as tele
m
o
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r
in
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tem
.
T
h
an
k
y
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u
to
Po
ts
d
am
Geo
f
o
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th
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esian
Me
teo
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lo
g
y
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lim
ato
lo
g
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a
n
d
Geo
p
h
y
s
ics
Ag
en
cy
,
f
o
r
p
r
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v
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in
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ea
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th
q
u
ak
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ata.
W
e
ex
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d
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p
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g
r
atitu
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Dir
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to
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h
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m
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v
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Dir
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ate
Gen
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Hig
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tio
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T
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R
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T
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an
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.
RE
F
E
R
E
NC
E
S
[
1
]
M
.
K
a
m
i
şl
i
o
ǧ
l
u
a
n
d
F
.
K
u
l
a
l
i
,
“
C
h
a
o
t
i
c
a
n
a
l
y
s
i
s
o
f
r
a
d
o
n
g
a
s
(
2
2
2
R
n
)
mea
s
u
r
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me
n
t
s
i
n
Le
s
v
o
s
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sl
a
n
d
:
d
e
t
r
e
n
d
e
d
f
l
u
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t
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o
n
a
n
a
l
y
si
s
(
D
F
A
)
,
”
7
t
h
I
n
t
e
rn
a
t
i
o
n
a
l
S
y
m
p
o
s
i
u
m
o
n
D
i
g
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t
a
l
F
o
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n
s
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c
s
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n
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c
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r
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y
,
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S
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S
D
F
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.
2
0
1
9
.
8
7
5
7
5
2
0
.
[
2
]
Z.
Q
i
a
o
,
G
.
W
a
n
g
,
H
.
F
u
,
a
n
d
X
.
H
u
,
“
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d
e
n
t
i
f
i
c
a
t
i
o
n
o
f
g
r
o
u
n
d
w
a
t
e
r
r
a
d
o
n
p
r
e
c
u
r
so
r
y
a
n
o
ma
l
i
e
s
b
y
c
r
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t
i
c
a
l
s
l
o
w
i
n
g
d
o
w
n
t
h
e
o
r
y
:
a
c
a
se
s
t
u
d
y
i
n
Y
u
n
n
a
n
r
e
g
i
o
n
,
S
o
u
t
h
w
e
st
C
h
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n
a
,
”
W
a
t
e
r
,
v
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.
1
4
,
n
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.
4
,
p
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1
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b
.
2
0
2
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,
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:
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3
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w
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4
0
4
0
5
4
1.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
1
4
,
No
.
1
,
Ma
r
c
h
2
0
2
5
:
39
-
45
44
[
3
]
T.
O
.
P
r
a
t
a
ma,
S
u
n
a
r
n
o
,
S
.
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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p
@u
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c
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Evaluation Warning : The document was created with Spire.PDF for Python.