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lv
es
th
e
cr
e
atio
n
o
f
f
lo
o
d
h
az
ar
d
m
a
p
s
,
wh
ich
h
elp
id
en
tify
ar
ea
s
at
r
is
k
o
r
p
r
o
n
e
to
f
lo
o
d
in
g
,
en
ab
lin
g
th
e
d
ev
elo
p
m
en
t
an
d
allo
ca
tio
n
o
f
ap
p
r
o
p
r
iate
m
ea
s
u
r
es
th
r
o
u
g
h
eith
er
s
tr
u
ctu
r
al
d
ef
en
s
es
o
r
lan
d
-
u
s
e
p
lan
n
in
g
[
4
]
.
T
ak
in
g
th
e
Ma
lan
g
r
eg
io
n
as
an
ex
am
p
le,
th
e
lack
o
f
co
m
p
r
eh
en
s
iv
e
f
l
o
o
d
d
ata
an
d
th
e
co
n
tin
u
o
u
s
ex
p
an
s
i
o
n
o
f
s
ettlem
en
ts
in
to
f
lo
o
d
-
p
r
o
n
e
zo
n
es
h
av
e
s
ig
n
if
ican
tly
in
cr
ea
s
ed
th
e
es
tim
ated
an
n
u
al
lo
s
s
es
d
u
e
to
f
lo
o
d
d
is
aster
s
.
T
h
er
e
f
o
r
e,
it
is
n
o
w
c
r
u
cial
t
o
co
n
d
u
ct
ass
ess
m
en
ts
o
f
ar
ea
s
v
u
ln
er
a
b
le
to
f
lo
o
d
in
g
b
y
d
e
v
elo
p
in
g
f
lo
o
d
v
u
ln
er
a
b
ilit
y
m
ap
s
th
at
h
ig
h
lig
h
t
an
d
r
a
n
k
t
h
e
lik
elih
o
o
d
o
f
f
lo
o
d
in
g
at
v
ar
y
in
g
s
ca
les.
Su
ch
m
ap
s
ca
n
aid
in
e
n
s
u
r
in
g
th
e
p
r
o
p
er
p
r
i
o
r
itizatio
n
o
f
ar
ea
s
in
u
r
g
en
t
n
ee
d
o
f
in
ter
v
en
tio
n
a
n
d
atten
tio
n
f
r
o
m
lo
c
al
g
o
v
er
n
m
en
ts
.
I
n
p
r
ev
i
o
u
s
s
tu
d
ies,
th
e
p
r
e
d
ictio
n
o
f
f
lo
o
d
-
p
r
o
n
e
ar
ea
s
h
as
in
v
o
lv
ed
v
a
r
io
u
s
h
y
d
r
o
lo
g
ical
o
r
s
tatis
t
ical
m
o
d
elin
g
f
r
am
ewo
r
k
s
.
Fo
r
in
s
tan
ce
,
r
ain
f
all
-
r
u
n
o
f
f
h
y
d
r
o
lo
g
ical
m
o
d
els
ar
e
am
o
n
g
th
e
m
o
s
t
co
m
m
o
n
m
eth
o
d
s
u
s
ed
to
esti
m
ate
f
lo
o
d
-
v
u
l
n
er
ab
le
r
eg
io
n
s
[
5
]
,
[
6
]
.
T
h
e
u
s
e
o
f
ac
cu
r
at
e
f
lo
o
d
p
r
ed
ictio
n
m
o
d
els
ca
n
s
ig
n
if
ican
tly
c
o
n
tr
ib
u
te
to
d
is
aster
m
an
ag
e
m
en
t
s
tr
ateg
ies,
p
o
licy
f
o
r
m
u
latio
n
,
an
d
th
e
p
r
io
r
itizatio
n
o
f
m
itig
atio
n
m
ea
s
u
r
es
f
o
r
ex
is
tin
g
h
az
a
r
d
s
.
R
ec
en
t
s
tu
d
ies
o
n
f
lo
o
d
p
r
ed
i
ctio
n
p
r
ed
o
m
in
an
tly
em
p
lo
y
s
p
ec
if
ic
d
ata
-
d
r
iv
e
n
m
o
d
els
th
at
in
co
r
p
o
r
ate
v
a
r
io
u
s
s
im
p
lifie
d
ass
u
m
p
tio
n
s
[
7
]
.
T
h
ese
m
o
d
els
ca
n
in
clu
d
e
p
h
y
s
ical,
d
ata
-
b
ased
,
an
d
m
ac
h
in
e
lea
r
n
in
g
ap
p
r
o
ac
h
es.
T
h
er
e
f
o
r
e,
th
e
r
esear
ch
p
r
o
b
lem
a
d
d
r
ess
ed
in
th
is
s
tu
d
y
is
h
o
w
to
d
esig
n
a
n
in
tellig
en
t
s
y
s
tem
f
o
r
p
r
e
d
i
ctin
g
f
lo
o
d
-
af
f
ec
ted
a
r
ea
s
b
ased
o
n
g
e
o
g
r
a
p
h
ic
in
f
o
r
m
atio
n
s
y
s
tem
(
GI
S)
d
ata
u
s
in
g
a
m
ac
h
i
n
e
lear
n
in
g
alg
o
r
ith
m
ap
p
r
o
ac
h
.
R
esear
ch
o
n
th
e
im
p
lem
en
tat
io
n
an
d
d
ev
el
o
p
m
en
t
o
f
m
ac
h
in
e
lear
n
in
g
m
o
d
els
f
o
r
f
lo
o
d
d
is
aster
p
r
ed
ictio
n
b
eg
a
n
in
ea
r
ly
2
0
1
8
,
with
th
e
f
ir
s
t
p
u
b
licatio
n
s
h
ig
h
lig
h
tin
g
th
e
u
s
e
o
f
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
[
8
]
–
[
1
0
]
.
I
n
th
is
in
itial
p
h
ase,
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
wer
e
em
p
lo
y
ed
to
m
o
d
el
an
d
p
r
ed
ict
f
lo
o
d
ev
en
ts
b
ased
o
n
v
a
r
io
u
s
r
elev
an
t
v
ar
iab
les,
s
u
ch
as
r
ain
f
all,
s
o
il
m
o
is
tu
r
e,
an
d
r
i
v
er
co
n
d
itio
n
s
.
Fo
r
ad
v
an
ce
,
th
e
r
esear
ch
ev
o
l
v
ed
t
o
in
teg
r
ate
m
u
lti
-
m
o
d
el
an
d
en
s
em
b
le
m
ac
h
in
e
lea
r
n
in
g
ap
p
r
o
ac
h
es
to
en
h
a
n
ce
th
e
ac
cu
r
ac
y
a
n
d
r
o
b
u
s
tn
ess
o
f
p
r
ed
ictio
n
s
.
T
h
ese
m
u
lti
-
m
o
d
e
l
an
d
en
s
em
b
le
tec
h
n
iq
u
es
in
v
o
lv
ed
c
o
m
b
in
i
n
g
s
ev
er
al
d
if
f
er
e
n
t
m
ac
h
in
e
lear
n
in
g
m
o
d
els
to
p
r
o
d
u
ce
m
o
r
e
r
eliab
le
p
r
ed
ictio
n
s
[
7
]
,
[
1
1
]
.
Ad
d
itio
n
ally
,
th
e
r
esear
ch
b
eg
a
n
in
c
o
r
p
o
r
atin
g
d
ata
f
r
o
m
GI
S
as
in
p
u
t
f
o
r
m
ac
h
in
e
lear
n
in
g
m
o
d
els.
GI
S
d
ata
p
r
o
v
id
es
m
o
r
e
d
etailed
an
d
s
p
ec
if
ic
g
eo
g
r
ap
h
ic
in
f
o
r
m
atio
n
,
s
u
ch
as
to
p
o
g
r
ap
h
y
,
lan
d
u
s
e,
an
d
wate
r
f
l
o
w
p
atter
n
s
,
wh
ich
ar
e
cr
u
cial
f
o
r
m
o
r
e
ac
cu
r
ate
ly
m
ap
p
in
g
f
lo
o
d
-
af
f
ec
ted
a
r
ea
s
.
B
y
lev
er
ag
in
g
GI
S
d
ata,
m
ac
h
in
e
lear
n
in
g
m
o
d
els
ca
n
g
en
er
ate
m
o
r
e
d
et
ailed
an
d
in
f
o
r
m
ativ
e
f
lo
o
d
r
is
k
m
ap
s
,
wh
ich
ar
e
ess
en
tial
f
o
r
ef
f
ec
tiv
e
d
is
aster
m
an
ag
em
en
t a
n
d
m
itig
atio
n
p
l
an
n
in
g
[
1
2
]
.
T
h
e
r
esear
ch
aim
s
to
ac
h
iev
e
two
p
r
im
ar
y
o
b
jectiv
es
.
F
ir
s
t,
to
d
ev
elo
p
a
p
r
e
d
ictiv
e
m
o
d
el
f
o
r
f
lo
o
d
-
af
f
ec
ted
ar
ea
s
b
y
le
v
er
ag
in
g
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
in
teg
r
ated
with
s
p
atial
d
ata
b
ased
o
n
GI
S,
an
d
s
ec
o
n
d
,
to
b
u
ild
a
web
-
b
ased
in
f
o
r
m
atio
n
m
an
a
g
em
en
t
s
y
s
tem
th
at
p
r
o
v
id
es
p
r
ed
ictio
n
s
o
f
f
lo
o
d
-
af
f
ec
te
d
ar
ea
s
u
s
in
g
m
ac
h
i
n
e
lear
n
i
n
g
alg
o
r
ith
m
s
a
n
d
GI
S
d
ata.
T
h
is
s
y
s
tem
is
d
esig
n
ed
to
o
f
f
er
a
r
eliab
le
an
d
ac
ce
s
s
ib
le
to
o
l
f
o
r
au
th
o
r
iti
es
an
d
co
m
m
u
n
ities
,
en
ab
li
n
g
m
o
r
e
ef
f
ec
tiv
e
f
lo
o
d
r
is
k
m
an
ag
em
e
n
t
an
d
m
itig
atio
n
s
tr
ateg
ies.
2.
M
E
T
H
O
D
Fig
u
r
e
1
illu
s
tr
ates
th
e
co
r
e
m
eth
o
d
o
l
o
g
y
f
o
r
d
ev
elo
p
i
n
g
th
is
s
y
s
tem
is
ce
n
ter
ed
o
n
m
ac
h
in
e
lear
n
in
g
,
s
p
ec
if
ically
u
tili
zin
g
r
an
d
o
m
f
o
r
est
(
R
F)
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SV
M)
alg
o
r
ith
m
s
f
o
r
p
r
ed
ictin
g
f
l
o
o
d
-
af
f
ec
ted
a
r
ea
s
.
I
n
th
e
d
ata
p
r
ep
r
o
ce
s
s
in
g
s
tag
e,
h
is
to
r
ical
f
lo
o
d
d
ata
is
ex
p
o
r
ted
in
to
s
h
a
p
ef
ile
f
o
r
m
at
an
d
co
m
b
in
ed
with
n
o
n
-
f
lo
o
d
d
ata.
T
h
ese
s
h
ap
ef
iles
ar
e
th
en
ass
ig
n
ed
v
alu
es
d
e
r
i
v
ed
f
r
o
m
GI
S
d
ata
,
s
u
ch
as
d
i
g
ital
elev
atio
n
m
o
d
e
l
(
DE
M)
,
asp
ec
t,
cu
r
v
atu
r
e
,
to
p
o
g
r
a
p
h
ic
wetn
ess
in
d
ex
(
T
W
I
)
,
a
n
d
s
lo
p
e,
b
ased
o
n
th
e
co
o
r
d
in
ates
o
f
f
lo
o
d
an
d
n
o
n
-
f
lo
o
d
p
o
in
ts
.
Du
r
in
g
th
e
tr
ain
in
g
p
h
ase,
th
e
p
r
ep
r
o
ce
s
s
ed
d
ata
is
d
iv
id
ed
in
to
th
r
ee
s
ets:
tr
ain
in
g
d
ata,
v
alid
atio
n
d
ata,
a
n
d
test
d
ata
[
1
3
]
.
On
ce
th
e
tr
ain
in
g
is
co
m
p
lete,
th
e
RF
an
d
SVM
alg
o
r
ith
m
s
ar
e
em
p
lo
y
e
d
to
p
r
ed
ict
f
lo
o
d
im
p
ac
ts
,
p
ar
ticu
lar
ly
f
o
r
th
e
Ma
lan
g
C
ity
,
u
s
in
g
h
is
to
r
ical
f
lo
o
d
d
ata
an
d
r
ele
v
an
t
g
eo
g
r
ap
h
ic
in
f
o
r
m
atio
n
as
in
p
u
ts
.
R
F
is
an
en
s
em
b
le
lear
n
in
g
m
eth
o
d
th
at
co
n
s
tr
u
cts
m
u
ltip
le
d
ec
is
io
n
tr
ee
s
d
u
r
in
g
tr
ain
in
g
an
d
o
u
tp
u
ts
th
e
m
o
d
e
o
f
th
e
class
es
(
f
o
r
class
if
icatio
n
)
o
r
m
ea
n
p
r
ed
ictio
n
(
f
o
r
r
e
g
r
ess
io
n
)
o
f
th
e
in
d
iv
id
u
al
tr
ee
s
[
1
4
]
,
[
1
5
]
.
I
t
is
r
o
b
u
s
t
to
o
v
er
f
itti
n
g
an
d
ca
n
h
an
d
le
lar
g
e
d
atasets
with
h
ig
h
d
im
en
s
io
n
ality
,
m
ak
in
g
it
s
u
itab
le
f
o
r
in
teg
r
atin
g
d
iv
er
s
e
GI
S
d
ata
in
p
u
ts
.
SVM
is
a
s
u
p
er
v
is
ed
lear
n
in
g
al
g
o
r
ith
m
th
at
f
in
d
s
th
e
o
p
tim
al
h
y
p
er
p
lan
e
to
s
ep
ar
ate
d
ata
in
t
o
class
es
[
1
6
]
.
I
t
is
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
P
r
ed
ictio
n
o
f flo
o
d
-
a
ffected
a
r
ea
s
b
a
s
ed
o
n
g
eo
g
r
a
p
h
ic
in
f
o
r
ma
tio
n
s
ystem
d
a
ta
u
s
in
g
…
(
A
mru
l F
a
r
u
q
)
4677
p
ar
ticu
lar
ly
ef
f
ec
ti
v
e
in
h
ig
h
-
d
im
en
s
io
n
al
s
p
ac
es
an
d
is
well
-
s
u
ited
f
o
r
b
in
a
r
y
class
if
icatio
n
task
s
,
s
u
ch
as
d
is
tin
g
u
is
h
in
g
b
etwe
en
f
lo
o
d
an
d
n
o
n
-
f
lo
o
d
a
r
ea
s
[
1
7
]
.
Fig
u
r
e
1
.
Ma
ch
i
n
e
lear
n
in
g
-
b
a
s
ed
m
o
d
el
f
o
r
f
lo
o
d
-
a
f
f
ec
ted
m
ap
f
o
r
ec
asti
n
g
His
to
r
ical
f
lo
o
d
d
ata
co
llectio
n
an
d
p
r
ep
ar
atio
n
f
o
r
m
t
h
e
f
o
u
n
d
atio
n
al
s
tep
in
d
ev
elo
p
in
g
th
e
f
l
o
o
d
p
r
ed
ictio
n
s
y
s
tem
.
Data
is
g
ath
er
ed
f
r
o
m
r
eliab
le
s
o
u
r
ce
s
,
s
u
ch
as
g
o
v
er
n
m
en
t
a
g
en
ci
es
lik
e
th
e
B
NP
B
,
Ma
lan
g
C
ity
as
well
as
m
eteo
r
o
lo
g
ical
d
ep
ar
tm
e
n
t
to
en
s
u
r
e
ac
cu
r
ac
y
a
n
d
r
elev
an
ce
[
1
8
]
.
T
h
is
h
is
to
r
ical
f
lo
o
d
d
ata,
wh
ich
in
clu
d
es
r
ec
o
r
d
s
o
f
p
ast
f
lo
o
d
ev
e
n
ts
,
is
ex
p
o
r
ted
in
to
s
h
ap
ef
ile
f
o
r
m
at,
a
s
tan
d
ar
d
GI
S
d
ata
f
o
r
m
at
th
at
s
to
r
es
g
eo
g
r
ap
h
ic
f
ea
tu
r
es
(
e
.
g
.
,
p
o
in
ts
,
lin
es
,
an
d
p
o
l
y
g
o
n
s
)
an
d
th
eir
ass
o
ciate
d
attr
ib
u
tes.
T
o
cr
ea
te
a
b
alan
ce
d
d
ataset
,
n
o
n
-
f
lo
o
d
d
ata
ar
ea
s
with
n
o
r
ec
o
r
d
ed
f
lo
o
d
ev
e
n
ts
is
also
co
llected
.
T
h
e
s
h
ap
ef
iles
ar
e
th
en
en
r
ich
ed
with
ad
d
itio
n
al
g
e
o
g
r
a
p
h
ic
attr
ib
u
tes,
s
u
ch
as
co
o
r
d
in
ates,
to
f
ac
ilit
ate
in
teg
r
atio
n
with
GI
S d
ata
lay
e
r
s
.
T
h
is
co
m
p
r
eh
e
n
s
iv
e
d
ataset
s
er
v
es a
s
th
e
in
p
u
t f
o
r
tr
ain
in
g
m
ac
h
in
e
lear
n
in
g
m
o
d
els,
en
ab
lin
g
th
em
to
le
ar
n
p
atter
n
s
an
d
r
elatio
n
s
h
ip
s
b
etwe
en
f
lo
o
d
o
cc
u
r
r
en
ce
s
an
d
e
n
v
ir
o
n
m
en
tal
f
ac
to
r
s
[
1
9
]
.
Pr
o
p
er
p
r
ep
a
r
atio
n
o
f
th
is
d
ata,
in
clu
d
in
g
h
a
n
d
lin
g
m
is
s
in
g
v
al
u
es
an
d
en
s
u
r
in
g
co
n
s
is
ten
cy
,
is
cr
itical
to
th
e
ac
cu
r
ac
y
an
d
r
el
iab
ilit
y
o
f
th
e
f
l
o
o
d
p
r
ed
ictio
n
s
y
s
tem
.
I
n
teg
r
atio
n
with
GI
S
d
ata
en
h
an
ce
s
th
e
f
lo
o
d
p
r
ed
ictio
n
m
o
d
el
b
y
i
n
co
r
p
o
r
atin
g
d
etailed
s
p
atial
in
f
o
r
m
atio
n
.
Key
GI
S
lay
er
s
,
s
u
ch
as
DE
M,
s
lo
p
e
,
T
W
I
,
a
s
p
ec
t,
an
d
cu
r
v
atu
r
e
,
ar
e
ex
tr
ac
ted
an
d
lin
k
ed
to
h
is
to
r
ical
f
lo
o
d
an
d
n
o
n
-
f
l
o
o
d
p
o
in
ts
b
ased
o
n
th
eir
c
o
o
r
d
in
ates.
DE
M
p
r
o
v
id
es
el
ev
atio
n
d
ata
,
s
lo
p
e
in
d
icate
s
ter
r
ain
s
teep
n
ess
,
T
W
I
m
ea
s
u
r
es
wate
r
ac
c
u
m
u
lat
io
n
p
o
ten
tial,
wh
ile
asp
ec
t
an
d
cu
r
v
atu
r
e
d
escr
ib
e
ter
r
ain
o
r
ie
n
tatio
n
a
n
d
s
h
a
p
e.
T
h
ese
attr
ib
u
tes
ca
p
t
u
r
e
e
n
v
ir
o
n
m
en
tal
a
n
d
t
o
p
o
g
r
ap
h
ical
f
ac
to
r
s
cr
itical
to
f
lo
o
d
d
y
n
am
ics,
en
r
ic
h
in
g
th
e
d
ataset
an
d
im
p
r
o
v
i
n
g
th
e
ac
cu
r
ac
y
o
f
m
ac
h
in
e
lear
n
in
g
m
o
d
els
in
p
r
ed
ictin
g
f
lo
o
d
-
p
r
o
n
e
ar
ea
s
[
2
0
]
.
T
h
e
p
r
ep
r
o
ce
s
s
ed
d
ataset
is
d
iv
id
ed
in
to
th
r
ee
d
is
tin
ct
s
u
b
s
ets
to
en
s
u
r
e
ef
f
ec
tiv
e
tr
ain
in
g
,
v
alid
atio
n
,
an
d
test
in
g
o
f
th
e
m
ac
h
in
e
lear
n
in
g
m
o
d
els.
T
h
e
lar
g
est
p
o
r
tio
n
,
tr
ain
i
n
g
d
ata
(
7
0
-
8
0
%),
is
u
s
ed
to
tr
ain
th
e
RF
an
d
SVM
m
o
d
e
ls
,
allo
win
g
th
em
to
lear
n
th
e
r
elatio
n
s
h
ip
s
b
etwe
en
in
p
u
t
f
ea
tu
r
es
(
e.
g
.
,
GI
S
d
ata)
an
d
th
e
tar
g
et
v
a
r
iab
le
(
f
lo
o
d
o
r
n
o
n
-
f
lo
o
d
)
.
A
s
m
aller
p
o
r
tio
n
,
v
alid
atio
n
d
ata
(
1
0
-
1
5
%),
is
r
eser
v
ed
f
o
r
tu
n
in
g
h
y
p
er
p
ar
am
eter
s
an
d
o
p
tim
izin
g
m
o
d
el
p
er
f
o
r
m
an
ce
,
en
s
u
r
in
g
th
e
m
o
d
els
ar
e
n
eit
h
er
o
v
er
f
itti
n
g
n
o
r
u
n
d
er
f
itti
n
g
th
e
d
ata.
Fin
ally
,
th
e
test
d
ata
(
10
-
1
5
%)
is
u
s
ed
to
ev
alu
ate
th
e
f
in
al
m
o
d
el'
s
ac
cu
r
ac
y
an
d
g
en
er
aliza
tio
n
a
b
ilit
y
,
p
r
o
v
id
i
n
g
an
u
n
b
iased
ass
ess
m
en
t o
f
h
o
w
well
th
e
m
o
d
el
p
er
f
o
r
m
s
o
n
u
n
s
ee
n
d
ata.
T
h
e
ev
alu
atio
n
m
eth
o
d
u
s
ed
f
o
r
class
if
icatio
n
in
th
is
s
tu
d
y
i
s
th
e
ar
ea
u
n
d
er
th
e
cu
r
v
e
(
A
UC
)
o
f
th
e
s
u
p
p
o
r
t
v
ec
to
r
class
if
ier
(
SVC
)
,
wh
ich
is
a
r
o
b
u
s
t
m
etr
ic
f
o
r
ass
ess
in
g
th
e
p
e
r
f
o
r
m
an
ce
o
f
b
in
a
r
y
class
if
icatio
n
m
o
d
els,
s
u
ch
as
f
lo
o
d
p
r
e
d
ictio
n
(
f
lo
o
d
v
s
.
n
o
n
-
f
lo
o
d
)
[
2
1
]
.
T
h
e
AUC
is
a
p
er
f
o
r
m
an
ce
m
etr
ic
d
er
iv
ed
f
r
o
m
th
e
r
ec
eiv
er
o
p
er
atin
g
ch
ar
ac
ter
is
tic
(
R
OC
)
cu
r
v
e,
wh
ich
p
lo
ts
th
e
tr
u
e
p
o
s
itiv
e
r
ate
(
T
PR
)
ag
ain
s
t
th
e
f
alse
p
o
s
itiv
e
r
ate
(
FP
R
)
at
v
ar
io
u
s
class
if
icati
o
n
th
r
esh
o
ld
s
.
T
h
e
AUC
p
r
o
v
id
es
a
s
in
g
le
s
ca
lar
v
alu
e
th
at
s
u
m
m
ar
izes
th
e
m
o
d
el'
s
ab
ilit
y
to
d
is
tin
g
u
is
h
b
etwe
en
th
e
two
class
es
(
f
lo
o
d
an
d
n
o
n
-
f
lo
o
d
)
.
T
h
e
SVC
is
a
s
u
p
er
v
is
ed
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
u
s
ed
f
o
r
b
in
ar
y
o
r
m
u
lti
-
class
class
if
icatio
n
task
s
.
I
n
th
is
s
tu
d
y
,
SVC
is
em
p
lo
y
ed
to
class
if
y
ar
ea
s
as
eit
h
er
f
lo
o
d
-
p
r
o
n
e
o
r
n
o
n
-
f
lo
o
d
-
p
r
o
n
e
b
ased
o
n
in
p
u
t
f
ea
tu
r
es d
er
iv
e
d
f
r
o
m
GI
S d
at
a
an
d
h
is
to
r
ical
f
lo
o
d
r
ec
o
r
d
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
4
,
No
.
6
,
Dec
em
b
er
2
0
2
5
:
4
6
7
5
-
4
6
8
3
4678
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
u
tili
za
tio
n
o
f
s
atellite
im
a
g
er
y
d
ata
f
o
r
m
a
p
p
in
g
th
e
Ma
lan
g
C
ity
ar
ea
is
ca
r
r
ied
o
u
t
th
r
o
u
g
h
th
e
in
teg
r
atio
n
o
f
SAS.Plan
et
an
d
Ar
cGI
S
ap
p
licatio
n
s
.
SAS.Plan
et
is
u
s
ed
as
a
to
o
l
to
d
o
wn
lo
ad
h
ig
h
-
r
eso
lu
tio
n
s
atellite
im
ag
er
y
,
wh
ich
is
th
en
im
p
o
r
ted
in
to
A
r
cGI
S
f
o
r
f
u
r
t
h
er
a
n
aly
s
is
an
d
v
is
u
aliza
tio
n
.
T
h
e
s
atellite
im
ag
er
y
o
f
Ma
lan
g
C
ity
o
b
ta
in
ed
f
r
o
m
SAS.Plan
et
is
p
r
o
c
ess
ed
in
Ar
cGI
S
to
g
e
n
er
ate
a
to
p
o
g
r
ap
h
ic
m
a
p
th
at
r
ep
r
esen
ts
th
e
ar
ea
'
s
e
lev
atio
n
b
ased
o
n
m
eter
s
ab
o
v
e
s
ea
lev
el
(
MA
SL)
.
T
h
is
ap
p
r
o
ac
h
h
as
b
ee
n
wid
ely
u
s
ed
in
g
eo
s
p
atial
r
esear
ch
,
as st
ated
b
y
[
2
2
]
,
[
2
3
]
to
s
u
p
p
o
r
t
s
p
atial
d
ata
m
an
ag
em
en
t a
n
d
d
is
aster
-
p
r
o
n
e
ar
ea
an
aly
s
is
.
T
h
e
im
p
lem
en
tatio
n
o
f
th
e
Ma
lan
g
C
ity
ar
ea
m
a
p
is
illu
s
tr
ated
in
Fig
u
r
e
2
.
Fig
u
r
e
2
(
a
)
s
h
o
ws
th
e
s
atellite
im
ag
e,
Fig
u
r
e
2
(
b
)
p
r
esen
ts
DE
M,
an
d
Fig
u
r
e
2
(
c)
d
is
p
lay
s
th
e
s
lo
p
e
m
ap
.
(
a)
(
b
)
(
c)
F
i
g
u
r
e
2
.
T
h
e
g
e
o
s
p
a
ti
a
l
i
n
f
o
r
m
a
t
i
o
n
d
a
t
a
of
(
a
)
s
at
e
ll
i
t
e
i
m
ag
e
,
(
b
)
D
E
M
,
a
n
d
(
c
)
s
l
o
p
e
T
h
e
r
aw
d
ata
is
co
n
v
er
ted
i
n
to
p
o
i
n
t
d
ata
u
s
in
g
QGI
S
an
d
s
to
r
ed
in
a
s
h
ap
e
f
ile
f
o
r
m
at.
T
h
is
p
r
o
ce
s
s
in
v
o
lv
es
ex
t
r
ac
tin
g
a
n
d
u
tili
zin
g
g
e
o
g
r
a
p
h
ic
c
o
o
r
d
in
ates
to
s
p
atially
v
is
u
alize
f
lo
o
d
ev
en
ts
in
QGI
S.
T
h
e
s
h
ap
ef
ile
f
o
r
m
at
is
c
h
o
s
en
f
o
r
its
h
ig
h
co
m
p
atib
ilit
y
with
o
th
er
g
eo
s
p
atial
m
ap
p
in
g
s
o
f
twar
e,
s
u
ch
as
Ar
cGI
S,
an
d
its
ab
ilit
y
to
s
to
r
e
v
ec
to
r
d
ata,
in
clu
d
i
n
g
attr
ib
u
te
in
f
o
r
m
atio
n
an
d
l
o
ca
tio
n
g
eo
m
et
r
y
.
A
p
r
ep
r
o
ce
s
s
in
g
s
tep
is
ap
p
lied
to
th
e
p
r
e
v
io
u
s
ly
g
en
e
r
ated
p
o
in
t d
ata
to
p
r
ep
ar
e
tr
ain
i
n
g
d
ata
f
o
r
th
e
m
ac
h
in
e
lear
n
in
g
m
o
d
el.
Ma
p
p
ed
f
lo
o
d
ev
en
t
lo
ca
tio
n
s
ar
e
ass
ig
n
e
d
th
e
lab
el
I
D
_
1
,
in
d
icatin
g
f
lo
o
d
-
af
f
ec
ted
ar
ea
s
.
As
a
co
m
p
ar
is
o
n
,
ad
d
itio
n
al
r
an
d
o
m
l
y
s
elec
ted
p
o
in
ts
ar
e
lab
eled
I
D_
0
,
r
ep
r
esen
tin
g
n
o
n
-
af
f
ec
ted
a
r
ea
s
.
T
h
ese
d
atasets
ar
e
co
m
b
in
ed
i
n
to
a
s
in
g
le
s
h
ap
ef
ile
f
o
r
ea
s
i
er
m
an
ag
em
e
n
t
an
d
co
m
p
atib
i
lity
with
g
eo
s
p
atial
s
o
f
twar
e.
T
h
e
p
r
o
ce
s
s
ed
d
ata
i
s
th
en
u
s
ed
f
o
r
tr
ain
in
g
th
e
m
ac
h
in
e
lear
n
i
n
g
m
o
d
el.
T
h
is
a
p
p
r
o
ac
h
alig
n
s
with
s
p
atial
d
ata
-
b
ased
g
eo
s
p
atial
s
tu
d
ies
b
y
[
2
4
]
wh
ich
em
p
h
a
s
ize
th
e
im
p
o
r
tan
ce
o
f
lab
elin
g
an
d
class
if
icatio
n
f
o
r
im
p
r
o
v
in
g
m
o
d
el
p
r
e
d
ictio
n
ac
cu
r
ac
y
.
T
h
e
GI
S
d
ata
u
s
ed
in
th
is
s
tu
d
y
in
clu
d
es
th
e
DE
M,
to
p
o
g
r
ap
h
ic
asp
ec
t,
cu
r
v
atu
r
e
,
s
lo
p
e,
T
W
I
,
d
is
tan
ce
to
r
o
ad
(
DT
R
o
ad
)
,
d
i
s
tan
ce
to
r
iv
er
(
DT
R
iv
er
)
,
an
d
d
is
tan
ce
to
d
r
ain
ag
e
(
DT
Dr
ai
n
ag
e)
.
All
d
atasets
ar
e
m
er
g
ed
i
n
to
a
s
in
g
le
s
h
ap
ef
ile
f
o
r
f
u
r
t
h
er
p
r
o
ce
s
s
in
g
in
m
ac
h
i
n
e
lear
n
in
g
m
o
d
elin
g
.
GI
S
d
ata
p
r
ep
r
o
ce
s
s
in
g
is
co
n
d
u
cted
u
s
in
g
Py
th
o
n
-
b
ased
p
r
o
g
r
am
s
.
I
n
th
is
s
tag
e,
DE
M
is
u
t
ilized
to
g
en
er
ate
co
o
r
d
in
ate
p
o
i
n
ts
co
v
er
in
g
t
h
e
en
tire
Ma
la
n
g
C
ity
ar
ea
.
T
h
ese
co
o
r
d
in
ate
p
o
in
ts
s
er
v
e
as
r
ef
er
en
ce
s
f
o
r
ex
tr
ac
tin
g
attr
ib
u
te
v
alu
es f
r
o
m
ea
ch
GI
S lay
er
.
Af
ter
war
d
,
th
ese
co
n
f
ig
u
r
atio
n
s
ca
n
b
e
tr
e
ated
as tr
ain
in
g
an
d
test
in
g
d
ata
as
il
lu
s
tr
ated
in
Fig
u
r
e
3
.
T
h
is
p
r
o
ce
s
s
en
s
u
r
es
th
at
ea
ch
co
o
r
d
in
ate
p
o
in
t
co
n
tain
s
r
elev
an
t
attr
ib
u
tes f
o
r
s
p
atial
an
aly
s
is
.
Fig
u
r
e
3
.
Flo
o
d
-
p
o
in
t d
ata
b
ef
o
r
e
an
d
af
ter
au
g
m
en
tatio
n
f
o
r
m
o
d
el
tr
ain
in
g
an
d
test
in
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
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tell
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SS
N:
2252
-
8
9
3
8
P
r
ed
ictio
n
o
f flo
o
d
-
a
ffected
a
r
ea
s
b
a
s
ed
o
n
g
eo
g
r
a
p
h
ic
in
f
o
r
ma
tio
n
s
ystem
d
a
ta
u
s
in
g
…
(
A
mru
l F
a
r
u
q
)
4679
3
.
1
.
Ra
nd
o
m
f
o
re
s
t
m
o
del e
v
a
lua
t
io
n
Acr
o
s
s
1
5
test
iter
atio
n
s
with
d
if
f
e
r
en
t
d
ata
s
p
lits
an
d
tr
ee
co
u
n
ts
,
th
e
R
F
m
o
d
el'
s
ac
cu
r
ac
y
r
a
n
g
ed
b
etwe
en
7
9
%
a
n
d
8
2
%,
d
e
m
o
n
s
tr
atin
g
c
o
n
s
is
ten
t
p
er
f
o
r
m
an
ce
d
esp
ite
v
ar
iatio
n
s
in
p
ar
am
eter
s
ettin
g
s
.
T
h
is
co
n
s
is
ten
cy
s
u
g
g
ests
th
at
th
e
m
o
d
el
ef
f
ec
tiv
ely
g
e
n
er
alize
s
f
lo
o
d
r
is
k
p
atter
n
s
with
o
u
t
s
ig
n
if
ican
t
o
v
er
f
itti
n
g
o
r
u
n
d
er
f
itti
n
g
.
Fi
g
u
r
e
4
s
h
o
ws
th
e
g
e
n
er
ated
f
lo
o
d
s
u
s
ce
p
tib
ilit
y
m
ap
v
is
u
a
lly
r
ep
r
esen
ts
r
is
k
lev
els
ac
r
o
s
s
th
e
s
tu
d
y
ar
ea
as
s
h
o
wed
b
y
Fi
gu
r
e
4
(
a)
.
Hig
h
-
r
is
k
f
lo
o
d
z
o
n
es
ar
e
m
ar
k
e
d
in
r
ed
,
in
d
icatin
g
ar
ea
s
with
a
s
ig
n
if
ican
t
lik
elih
o
o
d
o
f
f
lo
o
d
in
g
.
Mo
d
e
r
ate
-
r
is
k
zo
n
es
ap
p
ea
r
in
y
ello
w,
s
ig
n
if
y
in
g
r
eg
io
n
s
with
a
b
alan
ce
d
p
r
o
b
ab
ilit
y
o
f
f
l
o
o
d
o
cc
u
r
r
en
ce
.
Me
an
wh
ile,
l
o
w
-
r
is
k
zo
n
es
ar
e
s
h
ad
ed
in
g
r
ee
n
,
h
ig
h
lig
h
tin
g
ar
ea
s
with
m
in
im
al
f
lo
o
d
s
u
s
ce
p
tib
ilit
y
.
T
h
e
s
p
atial
d
is
tr
ib
u
t
io
n
o
f
t
h
ese
f
lo
o
d
-
p
r
o
n
e
ar
ea
s
alig
n
s
with
k
n
o
wn
g
eo
g
r
a
p
h
ic
an
d
h
y
d
r
o
lo
g
ical
ch
ar
ac
ter
is
tics
,
s
u
ch
as
p
r
o
x
i
m
ity
to
r
iv
er
s
,
d
r
ain
ag
e
c
h
an
n
els,
an
d
lo
w
-
ly
in
g
r
eg
io
n
s
.
T
h
e
r
esu
lts
f
u
r
t
h
er
v
alid
ate
th
e
in
teg
r
atio
n
o
f
GI
S
an
d
m
ac
h
in
e
lear
n
i
n
g
i
n
f
lo
o
d
p
r
ed
ictio
n
,
s
u
p
p
o
r
tin
g
its
ap
p
licatio
n
in
d
is
aster
r
is
k
m
an
ag
em
en
t a
n
d
u
r
b
an
p
lan
n
in
g
[
2
5
]
as d
ep
icted
in
Fig
u
r
e
4
(
b
)
.
(
a)
(
b
)
Fig
u
r
e
4
.
RF
m
o
d
el
r
esu
lt: (
a)
f
lo
o
d
-
a
f
f
ec
ted
ar
ea
an
d
(
b
)
m
o
d
el
p
er
f
o
r
m
an
ce
3
.
2
.
Su
pp
o
rt
v
ec
t
o
r
ma
chine m
o
del e
v
a
lua
t
i
o
n
T
h
e
SVM
m
o
d
el
was
test
ed
u
s
in
g
th
r
ee
d
if
f
e
r
en
t
k
er
n
el
ty
p
es:
r
ad
ial
b
asis
f
u
n
ctio
n
(
RBF
)
,
s
ig
m
o
id
,
an
d
p
o
ly
n
o
m
ial.
T
h
e
ac
cu
r
ac
y
v
ar
ied
ac
r
o
s
s
k
er
n
els,
with
R
B
F
an
d
p
o
ly
n
o
m
ial
ac
h
iev
i
n
g
s
im
ilar
ac
cu
r
ac
y
lev
els b
etwe
en
6
4
% a
n
d
7
0
%,
wh
ile
th
e
s
ig
m
o
id
k
er
n
el
p
e
r
f
o
r
m
ed
s
ig
n
if
ican
tly
w
o
r
s
e,
r
an
g
in
g
f
r
o
m
4
9
-
5
8
%.
T
h
e
f
lo
o
d
-
a
f
f
ec
ted
a
r
ea
a
n
d
m
o
d
el
p
er
f
o
r
m
an
ce
as
s
h
o
wn
in
Fig
u
r
e
5
.
Up
o
n
ev
alu
atin
g
th
e
r
esu
lts
,
th
e
R
B
F
k
er
n
el
p
r
o
d
u
ce
d
th
e
clea
r
est
f
lo
o
d
s
u
s
ce
p
tib
ilit
y
m
ap
co
m
p
ar
ed
to
th
e
o
th
e
r
k
er
n
els.
I
t
ef
f
ec
tiv
ely
d
is
p
lay
e
d
th
e
d
esig
n
ated
th
r
ee
-
class
f
lo
o
d
r
is
k
zo
n
es,
with
d
is
tin
ct
r
ed
(
h
i
g
h
r
is
k
)
,
y
ello
w
(
m
o
d
er
ate
r
is
k
)
,
a
n
d
g
r
ee
n
(
lo
w
r
is
k
)
a
r
ea
s
,
it
is
in
d
icate
d
in
Fig
u
r
e
5
(
a)
.
I
n
co
n
tr
ast,
b
o
th
th
e
s
ig
m
o
id
a
n
d
p
o
ly
n
o
m
ia
l
k
er
n
els
g
en
er
ated
m
ap
s
d
o
m
i
n
ated
b
y
y
ello
w,
in
d
icatin
g
a
n
o
v
er
g
e
n
er
aliza
tio
n
o
f
m
o
d
er
ate
f
lo
o
d
r
is
k
a
n
d
a
lack
o
f
clea
r
class
if
icatio
n
b
o
u
n
d
ar
ies.
T
h
ese
f
in
d
in
g
s
s
u
g
g
est
th
at
th
e
SVM
m
o
d
el
u
s
in
g
R
B
F
k
er
n
el
is
th
e
m
o
s
t
s
u
itab
le
f
o
r
f
lo
o
d
s
u
s
ce
p
tib
ilit
y
m
ap
p
in
g
in
th
is
s
tu
d
y
,
as
it
m
ain
tain
s
b
o
th
ac
cu
r
ac
y
a
n
d
i
n
ter
p
r
etab
ilit
y
[
2
6
]
.
T
h
ese
p
er
f
o
r
m
an
ce
s
as d
ep
ict
ed
in
Fig
u
r
e
5
(
b
)
.
3.
3
.
M
o
del’
s
perf
o
rma
nce
dis
cus
s
io
n
T
h
e
co
m
p
a
r
ativ
e
an
aly
s
is
o
f
t
h
e
f
lo
o
d
s
u
s
ce
p
tib
ilit
y
m
ap
s
g
en
er
ated
u
s
in
g
t
h
e
R
F
an
d
SVM
m
o
d
els
r
ev
ea
ls
k
ey
d
if
f
er
en
ce
s
in
p
r
e
d
ictiv
e
ac
cu
r
ac
y
an
d
s
p
atial
r
ep
r
esen
tatio
n
o
f
f
l
o
o
d
-
p
r
o
n
e
ar
ea
s
.
T
h
e
R
F
m
o
d
el
d
em
o
n
s
tr
ated
s
u
p
er
io
r
class
if
icatio
n
p
er
f
o
r
m
a
n
ce
,
ac
h
ie
v
in
g
an
ac
c
u
r
ac
y
o
f
8
2
%,
wh
e
r
e
as
th
e
SVM
m
o
d
el,
d
ep
en
d
i
n
g
o
n
th
e
k
er
n
el
ty
p
e
u
s
ed
,
e
x
h
ib
ited
lo
wer
a
n
d
m
o
r
e
v
ar
ia
b
le
ac
cu
r
ac
y
,
with
th
e
R
B
F
an
d
p
o
ly
n
o
m
ial
k
er
n
els
r
an
g
i
n
g
f
r
o
m
6
4
-
7
0
%
an
d
th
e
s
ig
m
o
id
k
er
n
el
p
er
f
o
r
m
in
g
th
e
wo
r
s
t
at
4
9
-
5
8
%.
T
h
ese
d
is
cr
ep
an
cies in
p
r
e
d
ictiv
e
ca
p
ab
ilit
y
d
ir
ec
tly
in
f
lu
en
ce
d
th
e
s
p
atial
d
elin
ea
tio
n
o
f
f
lo
o
d
-
p
r
o
n
e
r
e
g
io
n
s
.
T
h
e
R
F
-
b
ased
f
lo
o
d
s
u
s
ce
p
ti
b
ilit
y
m
ap
ex
h
ib
ited
a
well
-
d
ef
in
ed
class
if
icatio
n
o
f
f
lo
o
d
r
is
k
zo
n
es,
ef
f
ec
tiv
ely
ca
p
tu
r
in
g
th
e
h
ig
h
-
r
is
k
(
r
ed
)
,
m
o
d
er
ate
-
r
is
k
(
y
ell
o
w)
,
an
d
lo
w
-
r
is
k
(
g
r
ee
n
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ar
ea
s
with
clea
r
s
p
atial
b
o
u
n
d
ar
ies.
T
h
is
o
u
tc
o
m
e
ali
g
n
s
with
th
e
m
o
d
el’
s
ab
ilit
y
t
o
h
an
d
le
co
m
p
le
x
,
n
o
n
lin
ea
r
r
elatio
n
s
h
ip
s
with
in
h
ig
h
-
d
im
e
n
s
io
n
al
d
atasets
,
en
s
u
r
in
g
th
at
th
e
p
r
ed
ictiv
e
m
a
p
p
in
g
r
e
f
lects
r
ea
l
-
wo
r
ld
f
lo
o
d
d
is
tr
ib
u
tio
n
m
o
r
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
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4680
ac
cu
r
ately
.
Mo
r
eo
v
er
,
R
F'
s
al
g
o
r
ith
m
ap
p
r
o
ac
h
r
ed
u
ce
s
o
v
e
r
f
itti
n
g
an
d
in
cr
ea
s
es
g
en
er
ali
za
b
ilit
y
,
m
ak
in
g
it
a
r
o
b
u
s
t
ch
o
ice
f
o
r
g
eo
s
p
atial
f
lo
o
d
m
o
d
elin
g
.
C
o
n
v
e
r
s
ely
,
th
e
SVM
-
g
en
er
ated
f
lo
o
d
m
ap
s
v
ar
ied
in
in
ter
p
r
etab
ilit
y
d
e
p
en
d
in
g
o
n
th
e
k
er
n
el
ap
p
lied
.
T
h
e
R
B
F
k
er
n
el
p
r
o
d
u
ce
d
a
cl
ea
r
er
s
u
s
ce
p
tib
ilit
y
d
is
tr
ib
u
tio
n
co
m
p
a
r
ed
to
th
e
p
o
ly
n
o
m
ial
an
d
s
ig
m
o
id
k
e
r
n
els,
y
et
it
s
t
ill
lack
ed
th
e
d
is
ti
n
ct
zo
n
al
s
ep
ar
atio
n
ac
h
iev
ed
b
y
R
F.
No
tab
ly
,
m
ap
s
p
r
o
d
u
ce
d
b
y
th
e
p
o
ly
n
o
m
ial
an
d
s
ig
m
o
id
k
er
n
els
d
is
p
lay
ed
an
o
v
er
g
e
n
er
alize
d
class
if
icatio
n
,
with
an
e
x
ce
s
s
iv
e
d
o
m
in
a
n
ce
o
f
m
o
d
er
ate
-
r
is
k
(
y
ello
w)
ar
e
as,
s
u
g
g
esti
n
g
th
e
m
o
d
els
s
tr
u
g
g
led
to
d
ef
in
e
cle
ar
s
p
atial
b
o
u
n
d
a
r
ies.
T
h
is
r
esu
lt
m
ay
b
e
attr
ib
u
te
d
to
th
e
s
e
n
s
itiv
ity
o
f
SVM
to
class
im
b
alan
ce
s
an
d
its
r
elian
ce
o
n
k
er
n
el
-
b
ased
tr
a
n
s
f
o
r
m
atio
n
s
,
wh
ich
,
i
n
f
lo
o
d
m
ap
p
in
g
c
o
n
tex
ts
,
m
ig
h
t
n
o
t f
u
lly
ca
p
tu
r
e
t
h
e
in
tr
icate
s
p
atial
v
ar
iab
ilit
y
o
f
h
y
d
r
o
lo
g
ical
an
d
to
p
o
g
r
a
p
h
ical
f
ac
to
r
s
.
(
a)
(
b
)
Fig
u
r
e
5
.
SVM
m
o
d
el
r
esu
lt
of
(
a)
f
l
o
o
d
-
a
f
f
ec
ted
a
r
ea
an
d
(
b
)
m
o
d
el
p
er
f
o
r
m
a
n
ce
4.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
s
u
cc
ess
f
u
lly
d
ev
elo
p
ed
a
f
lo
o
d
p
r
e
d
ictio
n
m
o
d
el
b
y
in
te
g
r
atin
g
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
with
GI
S
-
b
ased
s
p
atial
d
ata.
T
h
e
m
o
d
el
aim
s
to
p
r
o
v
id
e
r
ap
i
d
an
d
ac
cu
r
ate
f
lo
o
d
s
u
s
ce
p
tib
ilit
y
ass
es
s
m
en
ts
,
o
f
f
er
in
g
v
al
u
ab
le
s
u
p
p
o
r
t
f
o
r
d
is
aster
m
itig
atio
n
an
d
r
is
k
m
a
n
ag
em
en
t.
B
y
le
v
er
ag
in
g
a
d
v
an
ce
d
co
m
p
u
tatio
n
al
tec
h
n
iq
u
es,
th
e
s
tu
d
y
en
h
a
n
ce
s
th
e
ca
p
ab
ilit
y
o
f
p
r
e
d
ictin
g
f
lo
o
d
-
p
r
o
n
e
ar
e
as,
wh
ich
is
cr
u
cial
f
o
r
ef
f
ec
tiv
e
p
la
n
n
in
g
an
d
d
ec
is
io
n
-
m
ak
in
g
.
T
h
e
e
x
p
er
im
e
n
t
al
r
esu
lts
in
d
icate
th
at
th
e
R
F
m
o
d
el
o
u
tp
er
f
o
r
m
s
th
e
SVM
m
o
d
el
in
b
o
th
p
r
ed
i
ctiv
e
ac
cu
r
ac
y
an
d
s
tab
ilit
y
.
R
F
ac
h
iev
ed
th
e
h
ig
h
est
ac
cu
r
ac
y
o
f
8
2
%
with
a
d
ata
s
p
lit
o
f
7
0
%
tr
ain
in
g
,
1
0
%
v
alid
atio
n
,
an
d
2
0
%
te
s
tin
g
u
s
in
g
2
0
0
d
ec
is
io
n
tr
e
es.
Acr
o
s
s
d
if
f
er
en
t
co
n
f
ig
u
r
atio
n
s
,
R
F m
ain
tain
ed
a
co
n
s
is
ten
tly
h
ig
h
ac
cu
r
ac
y
r
an
g
in
g
b
etwe
en
7
9
% a
n
d
8
2
%,
d
em
o
n
s
tr
atin
g
its
r
o
b
u
s
tn
ess
in
f
lo
o
d
s
u
s
ce
p
tib
ilit
y
m
o
d
elin
g
.
Me
an
wh
ile,
SVM's
p
er
f
o
r
m
an
ce
v
ar
ie
d
s
ig
n
if
ican
tly
d
ep
en
d
in
g
o
n
th
e
k
er
n
el
ty
p
e
u
s
ed
.
Am
o
n
g
th
e
test
ed
k
er
n
els,
th
e
R
B
F
k
er
n
el
y
ield
e
d
th
e
b
est
ac
cu
r
ac
y
at
6
8
%,
wh
ile
th
e
s
ig
m
o
id
k
er
n
el
h
ad
th
e
l
o
west
ac
cu
r
ac
y
at
4
9
%.
A
k
ey
ad
v
an
ta
g
e
o
f
R
F
lies
in
i
ts
ab
ilit
y
to
h
an
d
le
co
m
p
lex
,
n
o
n
lin
ea
r
r
elatio
n
s
h
ip
s
with
in
g
eo
s
p
atial
d
ata
wh
ile
m
ain
tain
in
g
h
ig
h
s
tab
ilit
y
ac
r
o
s
s
d
if
f
er
en
t
p
ar
am
eter
s
ettin
g
s
.
I
ts
e
n
s
em
b
le
lear
n
in
g
ap
p
r
o
ac
h
m
in
im
ize
s
o
v
er
f
itti
n
g
an
d
en
h
a
n
ce
s
th
e
r
eliab
ilit
y
o
f
f
lo
o
d
s
u
s
ce
p
tib
ilit
y
p
r
ed
ictio
n
s
.
On
th
e
o
th
er
h
an
d
,
SVM
s
h
o
wed
g
r
ea
ter
s
en
s
itiv
ity
to
k
er
n
el
s
e
lectio
n
,
lead
in
g
to
in
co
n
s
is
ten
cies
in
class
if
icatio
n
r
esu
lts
.
Ad
d
itio
n
ally
,
th
e
f
lo
o
d
s
u
s
ce
p
tib
ilit
y
m
ap
s
p
r
o
d
u
c
ed
b
y
R
F
ex
h
ib
ited
well
-
d
ef
in
ed
s
p
atial
class
if
icatio
n
s
,
ef
f
ec
tiv
ely
d
is
tin
g
u
is
h
in
g
b
etwe
en
h
ig
h
-
r
is
k
(
r
ed
)
,
m
o
d
er
ate
-
r
is
k
(
y
ello
w)
,
a
n
d
l
o
w
-
r
is
k
(
g
r
ee
n
)
ar
ea
s
.
I
n
co
n
tr
ast,
m
ap
s
g
e
n
er
ated
b
y
th
e
SVM
m
o
d
el,
p
ar
ti
cu
lar
ly
th
o
s
e
u
s
in
g
p
o
ly
n
o
m
ial
an
d
s
ig
m
o
id
k
e
r
n
els,
d
is
p
lay
ed
ex
ce
s
s
iv
e
d
o
m
in
an
ce
o
f
m
o
d
er
ate
-
r
is
k
zo
n
es,
s
u
g
g
esti
n
g
lim
itatio
n
s
in
ca
p
tu
r
in
g
s
p
ati
al
v
ar
iab
ilit
y
ac
c
u
r
ately
.
T
h
e
s
e
f
in
d
in
g
s
h
ig
h
lig
h
t
th
e
s
ig
n
if
ican
t
p
o
ten
tial
o
f
m
ac
h
in
e
lear
n
in
g
,
p
a
r
ticu
lar
ly
R
F,
in
f
lo
o
d
r
is
k
ass
ess
m
en
t.
T
h
e
s
tu
d
y
d
e
m
o
n
s
tr
ates
th
at
i
n
teg
r
atin
g
m
ac
h
in
e
lear
n
in
g
with
GI
S
ca
n
en
h
an
ce
p
r
ed
ictiv
e
ac
cu
r
ac
y
an
d
p
r
o
v
i
d
e
a
d
ata
-
d
r
iv
e
n
ap
p
r
o
ac
h
f
o
r
d
is
aster
p
r
ep
ar
e
d
n
ess
.
Fu
tu
r
e
r
esear
c
h
co
u
ld
ex
p
l
o
r
e
f
u
r
th
er
im
p
r
o
v
em
e
n
ts
,
s
u
ch
as
in
c
o
r
p
o
r
atin
g
a
d
d
itio
n
al
h
y
d
r
o
lo
g
ical
an
d
m
eteo
r
o
lo
g
i
ca
l
p
ar
am
eter
s
,
o
p
tim
izin
g
h
y
p
er
p
ar
am
eter
s
,
an
d
test
in
g
d
ee
p
lear
n
in
g
m
o
d
els
f
o
r
en
h
an
ce
d
p
er
f
o
r
m
an
ce
.
M
o
r
eo
v
e
r
,
in
teg
r
atin
g
r
ea
l
-
tim
e
f
lo
o
d
m
o
n
ito
r
in
g
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ata
an
d
ea
r
ly
war
n
in
g
s
y
s
tem
s
co
u
ld
f
u
r
t
h
er
s
tr
en
g
th
e
n
th
e
p
r
ac
tical
ap
p
licatio
n
o
f
m
ac
h
in
e
lear
n
in
g
-
b
ased
f
lo
o
d
p
r
ed
ictio
n
m
o
d
els
in
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J Ar
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n
tell
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8
9
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8
P
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a
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p
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A
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4681
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tu
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ef
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u
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ata,
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ee
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Un
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ee
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Tek
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M
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lay
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k
u
d
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a
m
p
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k
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lo
g
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M
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lay
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a
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la
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m
p
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r
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m
p
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s
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h
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t
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s
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c
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m
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re
c
e
iv
e
d
d
o
c
to
ra
te
i
n
Au
t
o
m
a
ti
c
Co
n
tr
o
l
Lab
o
ra
to
r
y
,
To
k
y
o
M
e
tro
p
o
li
tan
Un
i
v
e
rsity
i
n
2
0
1
5
.
He
is
n
o
w
with
th
e
M
a
lay
sia
-
Ja
p
a
n
In
tern
a
ti
o
n
a
l
In
stit
u
te
o
f
Tec
h
n
o
l
o
g
y
sin
c
e
2
0
1
2
.
He
wa
s
a
tt
a
c
h
e
d
to
Alc
o
n
Jo
h
o
r
(Ci
b
a
Visio
n
S
d
n
Bh
d
)
u
n
d
e
r
M
OH
E
CEO
fa
c
u
lt
y
p
ro
g
r
a
m
m
e
fro
m
F
e
b
2
0
2
0
-
A
u
g
2
0
2
0
.
He
is
a
se
n
io
r
m
e
m
b
e
r
o
f
IEE
E,
c
h
a
rted
e
n
g
in
e
e
r
fr
o
m
IE
T,
m
e
m
b
e
r
o
f
S
AE.
He
wa
s
re
c
ip
ien
t
o
f
a
n
As
ian
H
u
m
a
n
Re
so
u
rc
e
F
u
n
d
b
y
To
k
y
o
M
e
tr
o
p
o
li
tan
G
o
v
e
rn
m
e
n
t
fr
o
m
2
0
1
2
u
n
ti
l
2
0
1
5
.
His
field
o
f
re
se
a
rc
h
in
tere
st
in
c
lu
d
e
s
i
n
tell
ig
e
n
t
c
o
n
tr
o
l,
a
u
t
o
m
a
ti
c
a
n
d
ro
b
u
st
c
o
n
tr
o
l,
a
n
d
m
o
ti
o
n
c
o
n
tro
l,
wh
ich
re
late
d
to
a
p
p
li
c
a
t
io
n
s
o
f
p
o
siti
o
n
in
g
sy
ste
m
s,
v
e
h
i
c
le
d
y
n
a
m
ics
sy
ste
m
,
a
n
d
v
ib
ra
ti
o
n
a
n
d
c
o
n
tr
o
l
sy
ste
m
s.
No
w
e
x
p
a
n
d
in
g
to
I
o
T
a
n
d
m
a
c
h
i
n
e
lea
rn
in
g
a
p
p
li
c
a
ti
o
n
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
m
fit
ri.
k
l@u
tm.m
y
.
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