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22
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3
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1796
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ly
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x
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ste
m
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f
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d
isa
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K
ey
w
o
r
d
s
:
C
an
n
y
ed
g
e
d
etec
tio
n
C
o
m
p
u
ter
v
is
io
n
Flo
o
d
d
etec
tio
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I
m
ag
e
p
r
o
ce
s
s
i
n
g
So
b
el
ed
g
e
d
etec
tio
n
W
ater
lev
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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
:
Satr
i
y
o
B
u
d
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to
m
o
Dep
ar
t
m
en
t o
f
E
lectr
ical
an
d
C
o
m
p
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ter
E
n
g
in
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r
i
n
g
Un
i
v
er
s
ita
s
J
e
m
b
er
37
Kalim
a
n
ta
n
St.
,
Ka
m
p
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s
T
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al
B
o
to
,
6
8
1
2
1
,
I
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d
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m
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s
a
tr
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@
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.
ac
.
id
1.
I
NT
RO
D
UCT
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N
Flo
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d
is
aster
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f
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A
cc
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g
to
[
1
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d
[
2
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,
th
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s
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o
f
a
n
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ar
l
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w
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n
i
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m
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g
n
if
ica
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t
i
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d
is
as
ter
m
an
a
g
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m
e
n
t.
W
ar
n
in
g
s
ca
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o
u
t
as
an
ea
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ly
w
ar
n
i
n
g
s
y
s
te
m
b
ased
o
n
m
ea
s
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r
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m
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n
t
s
o
f
w
ater
lev
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l
in
r
iv
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s
an
d
d
a
m
s
.
Sev
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k
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n
d
s
o
f
R
esear
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h
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t
o
m
ea
s
u
r
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w
ater
lev
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ls
h
a
s
ac
co
m
p
li
s
h
ed
in
a
v
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iet
y
o
f
ap
p
licatio
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s
,
m
et
h
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d
s
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d
lo
ca
tio
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s
.
Se
v
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t
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p
es
o
f
d
etec
tio
n
d
ev
ices
d
ev
elo
p
ed
in
r
esear
ch
[
3
]
to
m
ea
s
u
r
e
w
ater
lev
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l.
Dete
ctio
n
o
f
w
ater
le
v
el
m
ea
s
u
r
e
m
e
n
ts
u
s
in
g
a
w
ater
g
a
u
g
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w
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d
o
n
e
i
n
m
o
u
n
tai
n
o
u
s
ar
ea
s
as
w
ell
[
4
]
,
[
5
]
an
d
tid
al
[
6
]
.
T
h
e
m
et
h
o
d
o
f
d
ata
ac
q
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is
itio
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as
also
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ca
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o
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t
b
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th
i
n
r
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-
ti
m
e
[
7
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an
d
n
o
t
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n
r
ea
l
-
ti
m
e
[
8
]
.
W
ate
r
lev
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m
ea
s
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r
e
m
en
t
tech
n
iq
u
es
h
a
v
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p
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m
ed
u
s
in
g
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ltra
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ic
s
en
s
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r
s
[
9
]
,
r
ad
a
r
w
a
v
es
[
1
0
]
,
an
d
ca
m
er
a
s
en
s
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r
s
[
1
1
]
.
T
h
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r
esear
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r
ev
ea
led
th
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co
m
p
u
tat
io
n
s
y
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it
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c
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v
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eu
r
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et
w
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r
k
(
C
NN
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m
eth
o
d
[
1
2
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b
ased
o
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I
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T
[
1
3
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,
[
1
4
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.
P
r
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p
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s
y
s
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m
f
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s
m
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cc
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en
c
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o
f
f
lo
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d
s
[
1
5
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th
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h
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u
s
e
o
f
s
e
n
s
o
r
s
i
n
m
ak
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n
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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J
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Sci
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N:
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[
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[
1
9
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an
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T
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T
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n
s
a
f
e
p
o
s
itio
n
.
T
h
e
m
o
n
ito
r
in
g
p
r
o
ce
s
s
at
th
e
w
ater
le
v
el
u
s
i
n
g
a
n
I
o
T
-
b
ased
ca
m
er
a
w
as
also
d
ev
elo
p
ed
[
2
2
]
.
T
h
e
f
lo
o
d
d
etec
tio
n
p
r
o
ce
s
s
is
s
e
n
t
v
ia
t
h
e
in
ter
n
et
a
n
d
s
e
n
t
to
ea
c
h
u
s
e
r
in
t
h
e
f
lo
o
d
d
etec
tio
n
p
r
o
ce
s
s
o
n
l
y
e
x
p
lain
s
t
h
e
p
r
o
to
ty
p
e
in
s
en
d
i
n
g
f
lo
o
d
d
ata
u
s
i
n
g
s
o
lar
ce
lls
a
s
a
p
o
w
er
s
u
p
p
l
y
f
r
o
m
th
e
ca
m
er
a.
T
h
e
p
r
o
ce
s
s
o
f
au
to
m
at
icall
y
d
etec
ti
n
g
f
lo
o
d
s
w
i
th
t
h
e
s
e
g
m
e
n
tat
io
n
m
e
th
o
d
u
n
d
er
f
lo
o
d
co
n
d
itio
n
s
is
d
is
cu
s
s
ed
[
2
3
]
.
T
h
e
au
to
m
at
ic
p
r
o
ce
s
s
ca
r
r
ied
o
u
t
u
s
in
g
I
o
T
in
th
e
p
r
o
ce
s
s
o
f
s
en
d
in
g
f
lo
o
d
d
etec
tio
n
s
ig
n
al
s
u
s
ed
s
en
t
to
ea
ch
u
s
er
.
W
h
en
w
a
ter
co
n
d
itio
n
s
h
av
e
a
co
lo
r
th
at
ex
ce
ed
s
t
h
e
r
ea
s
o
n
ab
le
a
m
o
u
n
t,
f
lo
o
d
in
g
d
etec
ted
th
r
o
u
g
h
a
ca
m
er
a.
Ho
w
ev
er
,
t
h
e
w
ater
co
n
d
itio
n
s
w
er
e
n
o
t
ex
p
lai
n
ed
in
d
etail
w
h
en
f
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o
d
s
o
cc
u
r
r
ed
.
T
h
e
f
lo
o
d
d
etec
tio
n
p
r
o
ce
s
s
u
s
in
g
v
ar
iat
io
n
s
in
th
e
m
o
v
e
m
en
t
o
f
a
s
i
n
g
le
ca
m
er
a
h
as
also
b
ee
n
esti
m
ated
b
y
[
3
]
w
it
h
i
m
a
g
e
p
r
o
ce
s
s
in
g
an
d
p
h
o
to
g
r
a
m
m
e
tr
y
tech
n
iq
u
es.
B
u
t
th
er
e
is
n
o
ex
p
lan
atio
n
y
e
t
ab
o
u
t
ef
f
o
r
ts
to
w
ar
n
ag
ain
s
t
f
l
o
o
d
s
.
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h
e
u
s
e
o
f
t
o
o
l
s
in
th
e
f
o
r
m
o
f
w
a
t
e
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l
ev
el
d
e
t
e
c
t
i
o
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w
it
h
a
w
at
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l
ev
e
l
m
a
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o
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th
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al
i
s
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c
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s
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eg
i
o
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of
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t
(
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I
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a
n
d
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s
eg
m
en
ta
t
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o
f
th
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w
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te
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e
l
in
d
i
c
at
o
r
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t
t
h
e
d
am
h
as
b
e
e
n
c
a
r
r
ie
d
o
u
t
b
y
[
2
4
]
.
T
h
i
s
p
r
o
c
e
s
s
r
e
q
u
i
r
es
c
l
ea
r
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o
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in
d
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c
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t
o
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i
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th
e
m
e
th
o
d
o
f
d
e
t
e
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tin
g
th
e
w
at
e
r
l
ev
el
.
T
h
e
p
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lem
t
h
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o
cc
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s
is
th
at
m
u
c
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o
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t
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t.
T
h
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m
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all
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SC
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ased
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all
f
lo
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r
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ater
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m
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n
ee
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.
2.
RE
S
E
ARCH
M
E
T
H
O
D
2
.
1
.
E
x
peri
m
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a
l s
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up
I
n
t
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r
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h
,
t
w
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m
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ts
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p
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f
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ed
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n
a
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y
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th
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f
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t
to
f
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t
t
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t r
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t
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all
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illed
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ater
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as
s
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o
w
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Fi
g
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r
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1
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s
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d
ex
p
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a
s
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n
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ater
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th
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n
tain
er
f
o
r
6
1
m
i
n
u
tes.
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ater
lev
el
d
etec
tio
n
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x
p
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m
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t
f
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r
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ar
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d
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b
y
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eter
m
i
n
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th
e
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h
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o
f
t
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w
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ter
lev
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to
t
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r
ee
lev
el
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a
m
el
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s
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l a
t
w
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1
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1
0
c
m
,
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a
ler
t l
ev
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w
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v
el
10
-
23
,
an
d
h
az
ar
d
lev
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th
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h
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h
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f
2
4
-
2
7
cm
.
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h
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m
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r
e
m
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n
t
w
as
m
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it
h
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as ill
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s
tr
ated
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F
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2
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u
r
e
1
.
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x
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u
r
e
2
.
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x
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s
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-
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p
f
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tio
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2
.
2
.
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d
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ig
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t
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r
e
3
.
Flo
w
c
h
ar
t f
lo
o
d
d
etec
tio
n
p
r
o
ce
s
s
2.
2.
1.
Co
nv
o
lutio
n
T
h
e
p
u
r
p
o
s
e
o
f
co
n
v
o
lu
tio
n
i
s
to
co
n
v
er
t i
m
ag
e
s
f
r
o
m
R
GB
t
o
g
r
a
y
s
ca
le.
C
o
n
v
o
lu
t
io
n
i
s
a
p
r
o
ce
s
s
in
w
h
ic
h
i
m
ag
e
s
m
a
n
ip
u
lated
u
s
in
g
e
x
ter
n
al
m
as
k
s
/
s
u
b
w
i
n
d
o
w
s
to
p
r
o
d
u
ce
n
e
w
i
m
a
g
es.
R
ed
,
g
r
ee
n
,
b
lu
e
(
R
GB
)
co
lo
r
s
p
ac
e
is
a
co
m
b
in
atio
n
o
f
p
r
i
m
ar
y
co
lo
r
s
,
n
a
m
el
y
r
ed
,
g
r
ee
n
,
a
n
d
b
lu
e,
co
m
m
o
n
l
y
u
s
ed
b
y
co
m
p
u
ter
m
o
n
ito
r
s
o
r
tele
v
i
s
i
o
n
s
.
T
h
e
r
esu
ltin
g
co
lo
r
co
m
e
s
f
r
o
m
a
co
m
b
i
n
atio
n
o
f
t
h
r
ee
co
lo
r
s
,
an
d
ea
c
h
h
as
a
v
al
u
e
o
f
eig
h
t
r
ed
b
its
,
e
ig
h
t
g
r
ee
n
b
it
s
,
an
d
ei
g
h
t
b
l
u
e
b
its
.
Mi
x
in
g
t
h
e
t
h
r
ee
p
r
i
m
ar
y
co
lo
r
s
w
it
h
a
n
eq
u
iv
ale
n
t
f
r
ac
tio
n
w
il
l p
r
o
d
u
ce
s
h
ad
es o
f
g
r
a
y
.
I
f
all
t
h
r
ee
c
o
lo
r
s
ar
e
f
u
ll
y
s
at
u
r
ated
,
it
w
il
l p
r
o
d
u
ce
w
h
ite.
2
.
2
.
2
.
Seg
m
ent
a
t
io
n
I
m
ag
e
s
eg
m
e
n
tatio
n
is
a
p
r
o
ce
s
s
t
h
at
ai
m
s
to
s
ep
ar
ate
th
e
f
o
r
eg
r
o
u
n
d
r
eg
io
n
f
r
o
m
t
h
e
b
ac
k
g
r
o
u
n
d
r
eg
io
n
.
T
h
is
s
ep
ar
atio
n
b
ased
o
n
th
e
s
tr
ik
in
g
d
if
f
er
en
ce
s
in
t
h
e
ch
ar
ac
ter
is
tic
s
o
f
ea
ch
ar
ea
.
A
s
f
o
r
th
e
i
m
ag
e
s
eg
m
e
n
tatio
n
p
r
o
ce
s
s
its
el
f
,
th
er
e
ar
e
s
ev
er
al
alg
o
r
ith
m
s
,
co
n
s
is
ti
n
g
of
p
o
in
t
,
lin
e
,
a
n
d
s
id
e
d
etec
tio
n
alg
o
r
ith
m
s
(
b
ased
o
n
R
o
b
er
t
o
p
er
ato
r
an
d
So
b
el
o
p
e
r
ato
r
)
.
I
n
[1
9
]
s
tates
th
at
s
e
g
m
e
n
tati
o
n
is
th
e
p
r
o
ce
s
s
o
f
d
iv
id
in
g
an
i
m
ag
e
i
n
to
s
ev
er
a
l
p
ar
ts
o
r
o
b
j
ec
ts
.
Seg
m
e
n
tati
o
n
h
as
a
v
er
y
s
ig
n
i
f
ica
n
t
p
ar
t
of
au
to
m
a
tic
i
m
a
g
e
an
al
y
s
is
b
ec
au
s
e
,
in
t
h
i
s
p
r
o
ce
d
u
r
e
,
th
e
d
esire
d
o
b
j
ec
t
w
ill
b
e
tap
p
ed
f
o
r
th
e
n
e
x
t
p
r
o
ce
s
s
,
f
o
r
ex
a
m
p
le
:
i
n
p
atter
n
r
ec
o
g
n
itio
n
.
T
h
e
s
eg
m
en
tatio
n
al
g
o
r
ith
m
b
ased
o
n
t
w
o
ch
ar
ac
ter
is
tic
s
o
f
th
e
d
eg
r
ee
o
f
im
a
g
e
b
r
ig
h
t
n
es
s
,
n
a
m
el
y
:
d
is
r
u
p
tio
n
an
d
co
n
f
o
r
m
it
y
[
25
]
.
I
n
th
e
f
i
r
s
t
ite
m
,
th
e
i
m
a
g
e
is
s
ep
ar
ated
/
d
iv
id
ed
b
ased
o
n
a
s
tr
ik
i
n
g
ch
a
n
g
e
f
r
o
m
th
e
d
e
g
r
ee
o
f
b
r
ig
h
t
n
ess
.
T
y
p
ical
a
p
p
licatio
n
s
ar
e
f
o
r
th
e
d
etec
t
i
o
n
o
f
p
o
in
t
s
,
lin
e
s
,
ar
ea
s
,
an
d
s
id
es
o
f
an
i
m
ag
e
.
I
n
t
h
e
s
ec
o
n
d
ca
te
g
o
r
y
,
it
b
ased
o
n
t
h
r
es
h
o
ld
in
g
,
r
eg
io
n
g
r
o
w
in
g
,
a
n
d
r
eg
io
n
sp
li
tti
n
g
an
d
m
er
g
in
g
.
T
h
e
p
r
in
cip
le
o
f
i
m
ag
e
s
e
g
m
en
tati
o
n
ap
p
lied
t
o
s
tatic
an
d
d
y
n
a
m
ic
i
m
a
g
es.
I
m
a
g
e
s
eg
m
e
n
tatio
n
i
s
d
iv
id
i
n
g
a
n
i
m
ag
e
i
n
to
h
o
m
o
g
e
n
eo
u
s
p
ar
t
s.
2.
2.
3
.
E
dg
e
d
e
t
ec
t
io
n
E
d
g
e
d
e
tectio
n
h
a
s
an
i
m
p
o
r
tan
t
r
o
le
to
d
etec
t
ed
g
es
th
a
t
li
m
it
t
w
o
h
o
m
o
g
en
eo
u
s
i
m
a
g
e
r
eg
io
n
s
th
a
t
h
av
e
d
if
f
er
en
t
b
r
ig
h
tn
e
s
s
lev
el
s
.
T
h
e
ai
m
is
to
co
n
v
er
t
2
D
i
m
ag
es i
n
to
c
u
r
v
ed
s
h
ap
es.
T
h
e
s
id
e
is
s
o
m
e
p
ar
t o
f
th
e
i
m
ag
e
w
h
er
e
t
h
e
b
r
ig
h
tn
e
s
s
i
n
te
n
s
i
t
y
c
h
a
n
g
e
s
d
r
asti
ca
ll
y
[
25
]
.
T
h
e
cr
itical
f
ac
to
r
in
ex
tr
ac
tin
g
f
ea
t
u
r
es
i
s
th
e
ab
ilit
y
to
d
etec
t
th
e
p
r
ese
n
ce
o
f
ed
g
e
s
o
f
o
b
j
ec
ts
in
t
h
e
i
m
a
g
e.
E
d
g
e
d
etec
tio
n
b
ec
o
m
es
t
h
e
f
ir
s
t
s
tep
to
en
co
m
p
as
s
i
n
f
o
r
m
atio
n
i
n
t
h
e
p
ictu
r
e.
E
d
g
es
c
h
ar
ac
ter
ize
t
h
e
b
o
u
n
d
ar
ies
o
f
o
b
j
ec
ts
an
d
ar
e
th
er
ef
o
r
e
u
s
ef
u
l
f
o
r
s
eg
m
en
tatio
n
a
n
d
id
en
ti
f
i
ca
tio
n
in
t
h
e
v
i
s
io
n
.
B
esid
es,
t
h
e
p
u
r
p
o
s
e
o
f
ed
g
e
d
etec
tio
n
is
to
in
cr
ea
s
e
t
h
e
ap
p
ea
r
an
ce
o
f
th
e
b
o
u
n
d
ar
y
li
n
es o
f
a
n
ar
ea
o
r
o
b
j
ec
t in
th
e
p
ictu
r
e.
a.
C
an
n
y
o
p
er
ato
r
T
h
er
e
ar
e
f
iv
e
s
tep
s
tak
e
n
to
i
m
p
le
m
e
n
t
C
an
n
y
ed
g
e
d
etec
ti
o
n
,
n
a
m
e
l
y
:
-
Step
1
is
f
ilter
in
g
o
f
t
h
e
i
m
ag
e
is
d
o
n
e
to
eli
m
i
n
ate
n
o
is
e
u
s
in
g
a
Ga
u
s
s
ia
n
f
ilter
w
it
h
s
i
m
p
le
le
v
els
w
i
t
h
th
e
p
r
o
v
is
io
n
s
t
h
at
th
e
s
tan
d
ar
d
s
u
s
ed
ar
e
m
u
ch
s
m
al
ler
th
a
n
th
e
s
ize
o
f
t
h
e
p
ictu
r
e.
-
Step
2
is
t
h
at
a
f
ter
s
m
o
o
t
h
i
n
g
th
e
i
m
a
g
e
a
g
ai
n
s
t
n
o
i
s
e
is
d
o
n
e,
t
h
en
th
e
p
r
o
ce
s
s
o
f
g
ett
in
g
ed
g
e
s
tr
e
n
g
t
h
(
ed
g
e
s
tr
en
g
th
)
u
s
i
n
g
th
e
Ga
u
s
s
ian
o
p
er
ato
r
.
I
m
a
g
e
g
r
ad
ien
ts
ca
lcu
lated
u
s
in
g
f
o
r
m
u
la
1.
|
|
=
|
|
+
|
|
(
1
)
-
Step
3
is
to
ca
lcu
late
th
e
ed
g
e
d
ir
ec
tio
n
.
T
h
e
f
o
r
m
u
la
u
s
ed
is
as sh
o
w
n
i
n
(
2
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
E
a
r
ly
w
a
r
n
in
g
flo
o
d
d
etec
to
r
a
d
o
p
tin
g
ca
mera
b
y
S
o
b
el
C
a
n
n
y
…
(
S
a
tr
yo
B
.
Uto
mo
)
1799
ℎ
=
−
1
(
)
(
2
)
-
Step
4
is
to
c
o
n
n
ec
t th
e
ed
g
e
d
ir
ec
tio
n
w
it
h
an
i
m
a
g
e
tr
ac
ea
b
le
p
ath
.
-
Step
5
is
th
e
H
y
s
ter
esi
s
p
r
o
ce
s
s
,
i.e
.
,
eli
m
i
n
at
i
n
g
b
r
o
k
en
l
in
e
s
.
B
ased
o
n
th
e
e
x
p
lan
atio
n
ab
o
v
e
s
tep
s
,
to
d
etec
t e
d
g
es
w
it
h
t
h
e
C
a
n
n
y
m
et
h
o
d
,
w
e
w
ill
u
s
e
a
g
r
ad
ien
t
G
(
x
,
y
)
,
w
h
ic
h
is
a
v
ec
to
r
co
n
s
is
tin
g
o
f
t
w
o
ele
m
e
n
ts
,
n
a
m
e
l
y
G
x
an
d
G
y
.
E
d
g
e
d
etec
tio
n
is
d
o
n
e
b
y
r
en
d
er
in
g
ea
ch
p
ix
el
in
t
h
e
im
ag
e
b
y
lear
n
i
n
g
f
r
o
m
th
e
t
o
p
lef
t
m
o
s
t
p
ix
el
(
n
o
r
th
ea
s
t)
an
d
m
o
v
i
n
g
to
th
e
b
o
tto
m
r
ig
h
t
m
o
s
t
p
i
x
el
(
s
o
u
t
h
w
e
s
t)
.
T
h
er
ef
o
r
e,
to
ass
is
t
w
it
h
ed
g
e
tr
ac
in
g
,
th
e
g
r
ad
ie
n
ts
o
f
Gx
an
d
G
y
ar
e
ca
lcu
lated
r
esp
ec
tiv
e
l
y
w
i
th
t
h
e
3
x
3
C
an
n
y
m
as
k
o
p
er
ato
r
m
atr
ix
,
as d
ep
icted
in
F
i
g
u
r
e
4
.
b.
So
b
el
o
p
er
at
o
r
Si
m
i
lar
to
C
an
n
y
,
th
e
g
r
ad
ien
t G
(
x
,
y
)
,
w
h
ich
i
s
a
v
ec
t
or
m
ad
e
o
f
t
w
o
ele
m
e
n
ts
,
G
x
an
d
G
y
,
is
u
s
ed
to
d
etec
t
ed
g
es
b
y
t
h
e
So
b
el
m
et
h
o
d
.
E
d
g
e
d
etec
tio
n
is
d
o
n
e
b
y
r
ea
d
in
g
ea
c
h
p
ix
el
o
n
t
h
e
i
m
a
g
e
b
y
lear
n
in
g
f
r
o
m
t
h
e
to
p
-
lef
t
p
ix
e
l
(
n
o
r
th
e
ast)
an
d
m
o
v
in
g
to
t
h
e
b
o
tto
m
r
i
g
h
t
m
o
s
t
p
ix
el
(
s
o
u
t
h
w
e
s
t)
.
T
h
er
ef
o
r
e,
to
h
el
p
tr
a
ce
th
e
ed
g
es,
th
e
g
r
ad
ien
ts
o
f
G
x
an
d
Gy
ar
e
ca
lcu
lated
u
s
in
g
th
e
3
x
3
So
b
el
Ma
s
k
m
atr
ix
m
et
h
o
d
.
T
h
is
m
et
h
o
d
tak
es
t
h
e
p
r
in
cip
le
o
f
th
e
lap
lacia
n
a
n
d
g
a
u
s
s
ian
f
u
n
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ili
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atr
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ed
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as s
h
o
w
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F
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g
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r
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5
.
Fig
u
r
e
4
.
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an
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r
Fig
u
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e
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.
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b
el
o
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p
lied
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el
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r
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m
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d
el
d
escr
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th
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d
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ig
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m
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o
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c.
SC
E
D
a
l
g
o
r
ith
m
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b
el
C
an
n
y
ed
g
e
d
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t
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n
(
SC
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D
)
a
l
go
r
ith
m
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s
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n
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w
m
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th
o
d
in
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g
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d
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t
h
at
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m
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s
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e
C
an
n
y
a
n
d
So
b
el
tech
n
iq
u
e
s
.
T
h
e
r
esu
lt
is
a
n
o
r
ig
i
n
al
m
et
h
o
d
th
at
p
r
o
v
id
es
ex
ce
llen
t
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lts
a
n
d
l
ess
n
o
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s
e.
T
h
e
co
n
ce
p
t
o
f
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D
m
atr
i
x
a
lo
g
ar
ith
m
is
q
u
ite
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le
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n
a
m
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l
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s
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el
m
et
h
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e
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et
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e
C
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d
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o
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e
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s
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An
ex
p
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a
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o
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it
h
m
m
et
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o
d
h
as b
ee
n
s
h
o
w
n
in
(
3
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,
(
4
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an
d
(
5
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.
-
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la
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Ho
r
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Ver
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4
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r
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S
C
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Ma
tr
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Ex
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[
1
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−
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[
−
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5
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T
h
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r
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lts
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in
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tw
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x
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tic
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s
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ex
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s
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g
p
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d
is
tr
ib
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tio
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.
2
.
4
.
Cent
ro
id
T
h
e
c
en
tr
o
id
is
th
e
ar
it
h
m
eti
c
m
ea
n
v
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o
f
a
n
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b
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t
'
s
s
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m
a
ll
p
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ts
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t
h
e
o
b
j
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t.
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ce
n
tr
o
id
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itio
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o
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o
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j
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t
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tr
ea
ted
as
th
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asic
m
ea
s
u
r
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v
alu
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o
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e
E
ig
e
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al
u
e.
So
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m
i
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th
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m
id
p
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t o
f
a
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o
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s
in
t
h
e
d
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p
r
o
ce
s
s
[
7
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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3
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5
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ted
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m
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es
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tio
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f
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a
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v
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ap
lace
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a
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d
cv
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el
.
[
4
]
.
3.
RE
SU
L
T
S
A
ND
D
I
SCU
SS
I
O
N
T
h
is
alg
o
r
it
h
m
h
a
s
u
s
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to
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m
b
in
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t
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f
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e
t
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b
el
o
p
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ato
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an
d
th
e
C
an
n
y
o
p
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r
ar
e
u
s
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to
d
etec
t
in
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m
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f
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d
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SC
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D
A
lo
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its
el
f
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et
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in
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m
atr
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f
o
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th
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So
b
el
o
p
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m
atr
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x
.
3
.
1
.
B
a
ll d
et
ec
t
io
n
Dete
ctio
n
o
f
th
e
b
all
wa
s
ca
r
r
ied
o
u
t
th
r
o
u
g
h
th
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d
etec
t
i
o
n
p
r
o
ce
s
s
u
s
in
g
th
e
alg
o
r
it
h
m
S
C
E
D
m
et
h
o
d
.
I
n
d
ig
ital
i
m
ag
e
p
r
e
-
p
r
o
ce
s
s
in
g
[
1
5
]
,
t
h
e
i
m
a
g
e
co
n
v
o
l
u
tio
n
p
r
o
ce
s
s
h
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d
l
ed
b
y
ch
an
g
i
n
g
t
h
e
R
GB
i
m
a
g
e
to
g
r
ay
s
ca
le.
Nex
t
to
th
e
s
eg
m
e
n
tatio
n
s
ta
g
e
w
it
h
m
a
s
k
in
g
tech
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iq
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es,
ed
g
e
d
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tio
n
is
d
o
n
e
u
s
i
n
g
th
e
SC
E
D
a
lg
o
r
it
h
m
.
T
h
e
r
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lts
o
f
m
a
s
k
in
g
d
etec
tio
n
s
h
o
w
ed
th
e
b
all
c
o
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ld
f
o
r
m
a
cir
cle,
ev
en
th
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h
th
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s
h
ap
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o
f
a
cir
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is
n
o
t
co
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tin
u
o
u
s
.
T
h
e
ex
is
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o
f
t
h
e
w
a
ter
s
u
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f
ac
e
ch
a
n
g
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t
h
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f
o
r
m
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f
th
e
f
lo
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g
b
all.
T
h
e
ch
an
g
e
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s
b
ec
au
s
e
t
h
e
ed
g
e
o
f
th
e
b
all
d
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ted
is
th
e
r
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lt
o
f
i
m
a
g
e
s
eg
m
e
n
tatio
n
w
h
e
n
g
o
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t
h
r
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u
g
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th
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p
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o
ce
s
s
o
f
c
h
an
g
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n
g
t
h
e
g
r
a
y
s
ca
le
i
m
a
g
e
,
a
s
s
ee
n
i
n
F
i
g
u
r
e
6
.
T
h
en
th
e
r
e
s
u
l
ts
o
f
t
h
e
d
etec
tio
n
ar
e
u
s
ed
to
d
eter
m
in
e
t
h
e
m
ea
n
v
alu
e
o
f
t
h
e
cir
cle
o
b
j
ec
t c
r
ea
te
d
s
o
th
at
th
e
o
b
j
ec
t c
an
b
e
d
is
p
lay
ed
.
T
h
e
d
etec
tio
n
p
r
o
ce
s
s
u
s
es
t
h
e
co
lo
r
o
f
th
e
b
all
w
it
h
t
h
e
o
r
an
g
e
li
n
e
b
ac
k
g
r
o
u
n
d
b
y
ad
j
u
s
ti
n
g
th
e
d
etec
tio
n
.
T
h
e
r
esu
lts
o
f
th
e
ap
p
licatio
n
o
f
d
etec
tio
n
o
n
l
y
d
etec
t
co
lo
r
s
o
n
th
e
b
all
b
ec
a
u
s
e
t
h
e
n
u
m
b
er
o
f
o
r
an
g
e
p
ix
el
s
o
n
t
h
e
b
all
a
n
d
b
a
ck
r
eg
io
n
h
a
v
e
d
if
f
er
en
t
r
es
o
lu
tio
n
s
.
T
h
is
p
ar
t
ca
u
g
h
t
in
t
h
e
i
m
ag
e
is
j
u
s
t
th
e
co
lo
r
o
f
th
e
b
all
b
y
f
o
r
m
i
n
g
th
e
ed
g
e
o
f
a
cir
c
le.
Me
an
w
h
ile,
th
e
ar
ea
w
a
s
n
o
t
d
etec
ted
d
u
e
to
n
o
t
m
ee
ti
n
g
t
h
e
p
ix
el
r
eso
lu
tio
n
th
r
e
s
h
o
ld
v
a
lu
e
.
T
h
e
b
ac
k
g
r
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u
n
d
is
n
o
t
r
ec
o
g
n
ized
b
ec
au
s
e
it
d
o
es
n
o
t
r
ea
ch
th
e
p
i
x
el
r
eso
lu
tio
n
t
h
r
es
h
o
ld
.
T
h
e
ex
p
er
i
m
en
t
w
as
d
o
n
e
as
w
ell
b
y
b
r
in
g
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n
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t
h
e
b
all
clo
s
er
to
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b
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k
g
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,
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lt
s
o
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te
s
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s
h
a
p
e
s
h
o
w
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h
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b
all
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eso
lu
t
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cr
ea
s
es.
A
d
j
u
s
t
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t
o
f
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m
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n
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b
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s
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to
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t
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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S
[1
]
J.
M
u
ly
o
n
o
,
“
T
h
e
th
re
e
p
il
lars
:
d
e
li
b
e
ra
ti
v
e
c
o
o
r
d
in
a
ti
o
n
m
a
n
a
g
e
m
e
n
t
m
o
d
e
l
(in
m
it
ig
a
ti
n
g
f
l
a
sh
f
lo
o
d
a
n
d
lan
d
slid
e
d
isa
ste
rs i
n
Je
m
b
e
r
Re
g
e
n
c
y
)
,”
Pro
c
e
e
d
in
g
B
o
o
k
V
o
l.
2
,
2
0
1
9
.
[2
]
A.
W
a
rd
h
o
n
o
,
M.
R
o
n
d
h
i
,
J.
F
.
Ir
a
wa
n
,
a
n
d
B.
P
ra
k
o
so
,
“
Id
e
n
ti
f
ica
ti
o
n
a
n
d
m
a
p
p
in
g
o
f
d
isa
ste
r
risk
o
f
f
las
h
f
lo
o
d
s
in
Je
m
b
e
r
Re
g
e
n
c
y
-
Eas
t
Ja
v
a
,
In
d
o
n
e
sia
,
”
In
ter
n
a
ti
o
n
a
l
S
y
mp
o
si
u
m,
M
icr
o
d
is a
n
d
Hu
e
Un
ive
rs
it
y
,
Vi
e
tn
a
m,
2
0
1
0
.
[3
]
Y
.
-
T
.
L
in
a
n
d
Y
.
-
C
.
L
in
A
s
a
d
a
,
“
A
u
to
m
a
ti
c
w
a
ter
-
le
v
e
l
d
e
tec
ti
o
n
u
sin
g
sin
g
le
-
c
a
m
e
ra
i
m
a
g
e
s
w
it
h
v
a
ried
p
o
se
s
,
”
M
e
a
su
re
me
n
t
,
v
o
l.
1
2
7
,
p
p
.
1
6
7
-
1
7
4
,
2
0
1
8
,
d
o
i:
1
0
.
1
0
1
6
/j
.
m
e
a
su
re
m
e
n
t.
2
0
1
8
.
0
5
.
1
0
0
.
[4
]
E.
Rid
o
lf
i
a
n
d
P
.
M
a
n
c
io
la
.
,
“
W
a
ter
lev
e
l
m
e
a
su
re
m
e
n
ts
f
ro
m
d
ro
n
e
s:
a
p
il
o
t
c
a
se
stu
d
y
a
t
a
d
a
m
sit
e
,
”
W
a
ter
,
v
o
l.
10
,
n
o
.
3
,
p
p
.
2
9
7
,
2
0
1
8
,
d
o
i:
1
0
.
3
3
9
0
/w
1
0
0
3
0
2
9
7
.
[5
]
Y.
H.
Zh
a
n
g
.
,
“
A
b
rief
d
isc
u
ss
i
o
n
o
n
m
o
d
e
l
se
lec
ti
o
n
o
f
w
a
t
e
r
lev
e
l
g
a
u
g
e
f
o
r
m
o
u
n
tain
riv
e
r
,
”
Au
to
m
.
W
a
ter
Res
o
u
r
ces
Hy
d
ro
lo
g
y
,
p
p
.
45
–
46
,
2
0
0
8
.
[6
]
Y.
Zh
a
n
g
a
n
d
Q.
S
h
e
n
,
“
A
p
p
li
c
a
ti
o
n
a
n
d
d
isc
u
ss
io
n
o
f
u
l
tras
o
n
ic
a
n
d
f
lo
a
t
ty
p
e
w
a
ter
lev
e
l
g
a
u
g
e
in
sl
u
ice
p
u
m
p
sta
ti
o
n
o
f
ti
d
a
l
riv
e
r
,
”
J
ia
n
g
su
W
a
ter
Res
o
u
rc
e
s,
v
o
l.
6
,
p
p
.
6
-
9
,
2
0
1
7
.
[7
]
T
.
M
.
T
h
e
k
k
il
,
e
t
a
l.
,
“
Re
a
l
-
ti
m
e
W
S
N
b
a
se
d
e
a
rl
y
f
lo
o
d
d
e
tec
ti
o
n
a
n
d
c
o
n
tr
o
l
m
o
n
it
o
rin
g
s
y
ste
m
,
”
2
0
1
7
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
In
te
ll
ig
e
n
t
C
o
mp
u
ti
n
g
,
I
n
stru
me
n
t
a
ti
o
n
a
n
d
Co
n
tro
l
T
e
c
h
n
o
lo
g
ies
(
ICICICT
)
,
2
0
1
7
,
d
o
i:
1
0
.
1
1
0
9
/ICICICT
1
.
2
0
1
7
.
8
3
4
2
8
2
8
.
[8
]
B.
B.
Na
ir
a
n
d
S
.
Ra
o
.
,
“
F
lo
o
d
W
a
ter
De
p
th
Esti
m
a
ti
o
n
-
A
S
u
rv
e
y
,
”
IEE
E
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Co
mp
u
t
a
ti
o
n
a
l
I
n
telli
g
e
n
c
e
a
n
d
C
o
mp
u
t
in
g
Res
e
a
rc
h
,
p
p
.
1
–
4
,
2
0
1
7
,
d
o
i:
1
0
.
1
1
0
9
/ICCIC.
2
0
1
6
.
7
9
1
9
5
7
3
.
[9
]
T.
M
.
T
sa
i
a
n
d
P.
H.
Ye
n
.
,
“
Im
p
ro
v
e
m
e
n
t
in
sta
g
e
m
e
a
su
rin
g
tec
h
n
iq
u
e
o
f
th
e
u
lt
ra
so
n
ic
s
e
n
so
r
g
a
u
g
e
,
”
M
e
a
su
re
me
n
t
,
v
o
l.
45
,
n
o
.
7
,
p
p
.
1
7
3
5
-
1
7
4
1
,
2
0
1
2
,
d
o
i:
1
0
.
1
0
1
6
/j
.
m
e
a
su
re
m
e
n
t.
2
0
1
2
.
0
4
.
0
1
2
.
[1
0
]
G.
B.
L
i,
e
t
a
l.
,
“
A
p
p
li
c
a
ti
o
n
o
f
g
u
id
e
d
-
w
a
v
e
ra
d
a
r
w
a
t
e
r
lev
e
l
m
e
ter
in
ti
d
a
l
lev
e
l
o
b
se
rv
a
ti
o
n
,”
J
o
u
rn
a
l
Oc
e
a
n
T
e
c
h
n
o
l
,
v
o
l.
37
,
p
p
.
19
-
23
,
2
0
1
8
.
[1
1
]
J
.
Yu
a
n
d
H
.
Ha
h
n
.
,
“
Re
m
o
te
d
e
tec
ti
o
n
a
n
d
m
o
n
it
o
ri
n
g
o
f
a
wa
te
r
lev
e
l
u
sin
g
n
a
rro
w
-
b
a
n
d
c
h
a
n
n
e
l
,
”
J
o
u
rn
a
l
o
f
In
fo
rm
a
t
io
n
S
c
ien
c
e
a
n
d
E
n
g
i
n
e
e
rin
g
, v
ol
.
2
6
,
p
p
.
71
-
82
,
2
0
1
0
.
[1
2
]
M.
A
n
b
a
ra
sa
n
,
B.
A
.
M
u
th
u
,
a
n
d
S
iv
a
p
a
rth
ip
a
n
,
“
De
tec
ti
o
n
o
f
F
lo
o
d
Disa
ste
r
S
y
ste
m
Ba
se
d
o
n
Io
T
,
Big
Da
ta
a
n
d
Co
n
v
o
l
u
ti
o
n
a
l
De
e
p
Ne
u
ra
l
N
e
tw
o
rk
,”
Co
mp
u
ter
Co
mm
u
n
ica
ti
o
n
s
,
v
o
l
.
1
5
0
,
p
p
.
1
5
0
–
157
,
A
u
g
2
0
1
9
,
d
o
i:
1
0
.
1
0
1
6
/j
.
c
o
m
c
o
m
.
2
0
1
9
.
1
1
.
0
2
2
.
[1
3
]
S
.
S
o
o
d
,
K.
R
.
S
a
n
d
h
u
,
K
.
S
in
g
la,
a
n
d
V
.
Ch
a
n
g
.
,
“
Io
T
,
b
ig
d
a
ta
a
n
d
H
P
C
b
a
se
d
sm
a
rt
f
lo
o
d
m
a
n
a
g
e
m
e
n
t
f
ra
m
e
w
o
rk
,
”
S
u
sta
in
a
b
le
Co
mp
u
ti
n
g
:
I
n
fo
rm
a
t
ics
a
n
d
S
y
ste
ms
,
v
o
l.
20
,
p
p
.
1
0
2
-
1
1
7
,
2
0
1
8
,
d
o
i:
1
0
.
1
0
1
6
/j
.
s
u
sc
o
m
.
2
0
1
7
.
1
2
.
0
0
1
.
[1
4
]
A
.
S
i
n
h
a
,
P
.
K
u
m
a
r
,
N
.
P
.
R
a
n
a
,
R
.
I
s
l
a
m
,
a
n
d
Y
.
K
.
D
w
iv
e
d
i
,
“
Im
p
a
c
t
o
f
t
h
e
i
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s
(
Io
T
)
i
n
D
i
s
a
s
t
e
r
M
a
n
a
g
e
m
e
n
t
:
A
ta
sk
-
t
e
c
h
n
o
l
o
g
y
f
it
p
e
r
s
p
e
c
t
iv
e
,
”
A
n
n
.
O
p
e
r
.
R
e
s
,
p
p
.
1
–
3
6
.
2
0
1
7
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
0
4
7
9
-
017
-
2
6
5
8
-
1
.
[1
5
]
G
.
F
u
rq
u
im
,
R
.
Ja
lali,
G
.
P
e
ss
in
,
R
.
P
a
z
z
i,
a
n
d
J
.
Ue
y
a
m
a
,
“
Ho
w
t
o
im
p
ro
v
e
f
a
u
lt
to
lera
n
c
e
in
d
isa
ste
r
p
re
d
ictio
n
s
:
A
c
a
s
e
stu
d
y
a
b
o
u
t
f
las
h
f
lo
o
d
s
u
sin
g
I
o
T
,
M
L
,
a
n
d
re
a
l
d
a
ta
,
”
S
e
n
so
rs
,
v
o
l.
18
,
n
o
.
3
,
p
p
.
9
0
7
,
2
0
1
8
,
d
o
i:
1
0
.
3
3
9
0
/s
1
8
0
3
0
9
0
7
.
[1
6
]
N
.
M
a
sp
o
,
e
t
a
l
.
,
“
De
v
e
lo
p
m
e
n
t
o
f
In
tern
e
t
o
f
T
h
in
g
(Io
T
)
tec
h
n
o
l
o
g
y
f
o
r
f
lo
o
d
p
re
d
icti
o
n
a
n
d
e
a
rl
y
w
a
rn
in
g
s
y
ste
m
(E
W
S
)
,
”
In
t.
J
.
I
n
n
o
v
.
T
e
c
h
n
o
l.
Ex
p
lo
re
.
E
n
g
,
v
o
l.
8
,
p
p
.
2
1
9
-
2
2
8
,
2
0
1
8
.
[1
7
]
T
.
De
M
a
rc
o
,
a
n
d
D
.
Ca
z
z
a
to
,
“
Ra
n
d
o
m
ize
d
c
ircle
d
e
tec
ti
o
n
w
it
h
iso
p
h
o
tes
c
u
rv
a
tu
re
a
n
a
l
y
sis
,
”
Pa
tt
e
rn
Rec
o
g
n
it
io
n
,
v
o
l.
4
8
,
n
o
.
2
,
p
p
.
4
1
1
-
4
2
1
,
2
0
1
5
,
d
o
i:
1
0
.
1
0
1
6
/j
.
p
a
tco
g
.
2
0
1
4
.
0
8
.
0
0
7
.
[1
8
]
M
.
A
q
il
,
M
.
F
u
a
d
,
M
.
Ru
d
d
i
n
,
A
.
G
h
a
n
i,
a
n
d
T
o
le
S
u
ti
k
n
o
.
,
“
A
Re
v
ie
w
o
n
M
e
th
o
d
s
o
f
Id
e
n
t
ify
in
g
a
n
d
Co
u
n
ti
n
g
A
e
d
e
s
Ae
g
y
p
ti
L
a
rv
a
e
u
sin
g
I
m
a
g
e
S
e
g
m
e
n
tatio
n
T
e
c
h
n
iq
u
e
,
”
T
EL
KOM
NIKA
T
e
lec
o
mm
u
n
ica
ti
o
n
,
Co
m
p
u
ti
n
g
,
El
e
c
tro
n
ics
a
n
d
C
o
n
tro
l
, v
o
l.
1
5
,
n
o
.
3
,
p
p
.
1
1
9
9
-
1
2
0
6
,
2
0
1
7
,
d
o
i:
1
0
.
1
2
9
2
8
/
telk
o
m
n
ik
a
.
v
1
5
i3
.
6
4
2
2
.
[1
9
]
R
.
M
u
th
u
k
rish
n
a
n
a
n
d
M
.
Ra
d
h
a
.
,
“
Ed
g
e
d
e
tec
ti
o
n
tec
h
n
i
q
u
e
s
f
o
r
im
a
g
e
se
g
m
e
n
tatio
n
”
,
I
n
ter
n
a
t
io
n
a
l
J
o
u
r
n
a
l
o
f
Co
mp
u
ter
S
c
ien
c
e
&
In
fo
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
,
v
ol
.
3
,
n
o
.
6
,
p
p
.
2
5
9
-
2
6
7
,
2
0
1
1
,
d
o
i:
1
0
.
5
1
2
1
/
ij
c
sit.
2
0
1
1
.
3
6
2
0
.
[2
0
]
V
.
W
il
e
y
a
n
d
T
.
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