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9
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12
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s
iv
e
d
ee
p
-
lear
n
in
g
ar
ch
itectu
r
es,
th
is
wo
r
k
p
r
o
p
o
s
es
an
i
n
teg
r
ated
I
o
T
–
clo
u
d
–
ML
f
r
am
ewo
r
k
s
p
e
cif
ically
d
esig
n
ed
f
o
r
d
ep
lo
y
m
en
t
in
f
r
ag
ile
an
d
in
ter
m
itten
tly
s
u
p
p
lied
wate
r
d
is
tr
ib
u
tio
n
s
y
s
tem
s
.
T
h
e
p
r
o
p
o
s
ed
s
o
lu
tio
n
lev
er
a
g
es
d
is
tr
ib
u
ted
I
o
T
s
en
s
o
r
s
f
o
r
r
ea
l
-
tim
e
ac
q
u
is
itio
n
o
f
f
l
o
w
an
d
p
r
ess
u
r
e
d
ata,
a
r
an
d
o
m
f
o
r
est
m
o
d
el
f
in
e
-
tu
n
ed
t
o
Har
ar
e’
s
h
is
to
r
ical
d
em
an
d
p
atter
n
s
f
o
r
p
r
ed
ictiv
e
an
aly
tics
,
an
d
a
clo
u
d
-
h
o
s
t
ed
ASP.NE
T
p
latf
o
r
m
f
o
r
e
x
ec
u
tin
g
au
to
m
ate
d
v
alv
e
-
co
n
t
r
o
l d
ec
is
io
n
s
th
r
o
u
g
h
lo
w
-
co
s
t u
ltra
s
o
n
ic
ac
tu
at
o
r
s
.
T
h
e
co
n
tr
ib
u
tio
n
s
o
f
th
is
s
tu
d
y
ar
e
f
o
u
r
f
o
ld
:
−
A
q
u
an
tifie
d
ch
a
r
ac
ter
izatio
n
o
f
Har
ar
e
’
s
wate
r
d
is
tr
ib
u
tio
n
in
e
f
f
icien
cies,
h
i
g
h
lig
h
tin
g
o
p
er
atio
n
al
co
n
s
tr
ain
ts
n
o
t a
d
d
r
ess
ed
b
y
c
o
n
v
en
tio
n
al
SC
ADA
-
b
ased
ap
p
r
o
ac
h
es;
−
D
ev
elo
p
m
en
t
o
f
a
r
an
d
o
m
f
o
r
est
p
r
ed
ictiv
e
m
o
d
el
tailo
r
ed
to
Har
ar
e’
s
co
n
s
u
m
p
tio
n
an
d
p
r
ess
u
r
e
d
y
n
am
ics;
−
I
n
teg
r
atio
n
o
f
I
o
T
s
en
s
in
g
,
clo
u
d
co
m
p
u
tin
g
,
an
d
ML
i
n
tellig
en
ce
in
to
a
u
n
if
ied
r
e
al
-
tim
e
v
alv
e
-
au
to
m
atio
n
ar
c
h
itectu
r
e;
an
d
−
E
m
p
ir
ical
ev
alu
atio
n
d
em
o
n
s
tr
atin
g
im
p
r
o
v
e
d
f
o
r
ec
ast
in
g
ac
cu
r
ac
y
,
en
h
an
ce
d
a
n
o
m
aly
-
d
etec
tio
n
p
r
ec
is
io
n
,
an
d
o
p
e
r
atio
n
al
b
e
n
ef
its
o
v
er
ex
is
tin
g
m
a
n
u
al
an
d
s
em
i
-
au
to
m
ated
p
r
ac
tices.
T
h
e
r
em
ain
d
er
o
f
th
is
p
ap
er
is
o
r
g
an
ized
as:
s
ec
tio
n
I
I
p
r
e
s
en
ts
th
e
m
eth
o
d
s
u
s
ed
;
s
ec
tio
n
I
I
I
d
escr
ib
es
t
h
e
r
esu
lts
an
d
f
in
d
in
g
s
; a
n
d
s
ec
tio
n
I
V
co
n
cl
u
d
es th
e
s
tu
d
y
an
d
o
u
tlin
es f
u
tu
r
e
r
esear
ch
d
ir
ec
tio
n
s
.
2.
M
E
T
H
O
D
2
.
1
.
Da
t
a
c
o
llect
io
n
T
h
e
p
r
e
d
ictiv
e
m
o
d
el
f
o
r
o
p
ti
m
izin
g
wate
r
d
is
tr
ib
u
tio
n
in
Har
ar
e
u
tili
ze
s
a
co
m
p
r
e
h
en
s
iv
e
d
ataset
o
b
tain
ed
f
r
o
m
th
e
C
ity
o
f
Har
ar
e
wate
r
d
is
tr
ib
u
tio
n
r
ec
o
r
d
s
[
12
]
.
T
h
is
d
ataset
is
s
tr
u
ctu
r
e
d
in
tab
u
lar
f
o
r
m
at
an
d
in
clu
d
es
k
ey
f
ea
tu
r
es
s
u
ch
as
d
ate,
tim
e,
ar
ea
,
wate
r
f
lo
w,
p
r
ess
u
r
e,
an
d
co
n
s
u
m
p
tio
n
.
E
ac
h
en
tr
y
r
ep
r
esen
ts
a
s
n
a
p
s
h
o
t
o
f
wate
r
d
is
tr
ib
u
tio
n
p
ar
am
eter
s
at
s
p
ec
if
ic
lo
ca
tio
n
s
an
d
tim
es
,
en
a
b
lin
g
d
etailed
an
aly
s
is
o
f
p
atter
n
s
an
d
an
o
m
alies.
His
to
r
ical
d
ata
s
p
an
n
in
g
m
u
ltip
le
y
ea
r
s
wer
e
co
m
b
in
ed
with
r
ea
l
-
tim
e
r
ea
d
in
g
s
f
r
o
m
I
o
T
s
en
s
o
r
s
d
ep
lo
y
ed
th
r
o
u
g
h
o
u
t
t
h
e
city
[
12
],
[
13
]
,
[
15
].
As
illu
s
tr
ated
in
F
ig
u
r
e
1
,
th
e
o
v
er
all
wo
r
k
f
lo
w
b
eg
in
s
with
d
ata
co
l
lectio
n
f
r
o
m
h
is
to
r
ical
r
ec
o
r
d
s
an
d
I
o
T
s
en
s
o
r
s
,
f
o
llo
wed
b
y
p
r
ep
r
o
ce
s
s
in
g
an
d
f
ea
tu
r
e
e
n
g
in
ee
r
i
n
g
s
tag
es.
Da
ta
p
r
ep
r
o
ce
s
s
in
g
in
v
o
lv
e
d
cl
ea
n
in
g
m
is
s
in
g
v
alu
es,
n
o
r
m
a
lizatio
n
,
an
d
f
ea
t
u
r
e
s
elec
tio
n
to
en
h
a
n
ce
m
o
d
el
p
er
f
o
r
m
a
n
ce
[
11
]
,
[
13
]
.
R
elev
a
n
t
f
ea
tu
r
es
wer
e
en
g
in
ee
r
ed
t
o
ca
p
tu
r
e
tem
p
o
r
al
p
atter
n
s
,
in
clu
d
in
g
lag
v
ar
iab
l
es f
o
r
wate
r
d
em
a
n
d
a
n
d
r
o
llin
g
av
er
ag
es f
o
r
f
l
o
w
an
d
p
r
ess
u
r
e.
Fig
u
r
e
1
.
Data
co
llectio
n
,
p
r
e
p
r
o
ce
s
s
in
g
,
m
o
d
el
t
r
ain
in
g
,
an
d
d
ep
lo
y
m
en
t w
o
r
k
f
lo
w
f
o
r
wat
er
d
is
tr
ib
u
tio
n
o
p
tim
izatio
n
2
.
2
.
M
o
del
des
ig
n a
nd
a
lg
o
r
it
hm
s
T
h
e
co
r
e
o
f
th
e
Har
ar
e
wate
r
d
is
tr
ib
u
tio
n
o
p
tim
izatio
n
s
y
s
tem
is
a
s
u
p
er
v
is
ed
ML
m
o
d
el
d
esig
n
ed
to
p
r
ed
ict
wate
r
d
em
an
d
,
d
etec
t
an
o
m
alies
s
u
ch
as
leak
s
,
an
d
p
r
o
v
id
e
ac
tio
n
ab
le
r
ec
o
m
m
e
n
d
atio
n
s
f
o
r
r
ea
l
-
tim
e
v
alv
e
co
n
tr
o
l [
3
]
,
[
7
]
,
[
17
].
Evaluation Warning : The document was created with Spire.PDF for Python.
C
o
m
p
u
t Sci
I
n
f
T
ec
h
n
o
l
I
SS
N:
2722
-
3
2
2
1
Op
timiz
in
g
w
a
ter d
is
t
r
ib
u
tio
n
in
Ha
r
a
r
e,
Zimb
a
b
w
e
u
s
in
g
I
o
T a
n
d
clo
u
d
co
mp
u
tin
g
(
A
n
g
el
in
e
Ts
a
ts
a
)
233
2
.
2
.
1
.
Ra
nd
o
m
f
o
re
s
t
a
lg
o
rit
hm
T
h
e
m
o
d
el
e
m
p
lo
y
s
a
r
a
n
d
o
m
f
o
r
est
al
g
o
r
ith
m
d
u
e
to
its
r
o
b
u
s
tn
ess
in
h
a
n
d
lin
g
co
m
p
lex
,
n
o
n
lin
ea
r
r
elatio
n
s
h
ip
s
an
d
its
p
r
o
v
en
p
er
f
o
r
m
a
n
ce
in
b
o
th
r
e
g
r
ess
io
n
an
d
class
if
icatio
n
task
s
[
1
]
,
[
18
]
.
R
an
d
o
m
f
o
r
est
is
well
-
s
u
ited
f
o
r
tim
e
-
s
er
ies
f
o
r
ec
as
tin
g
a
n
d
an
o
m
aly
d
etec
tio
n
,
cr
itical
f
o
r
r
ea
l
-
tim
e
u
r
b
an
wate
r
m
an
ag
em
en
t.
T
h
e
h
y
p
er
p
ar
a
m
eter
s
,
in
clu
d
in
g
th
e
n
u
m
b
er
o
f
tr
ee
s
,
m
ax
im
u
m
d
e
p
th
,
a
n
d
m
in
im
u
m
s
am
p
les
p
er
s
p
lit,
wer
e
f
in
e
-
t
u
n
ed
u
s
in
g
cr
o
s
s
-
v
alid
atio
n
t
o
m
ax
im
iz
e
p
r
ed
ictiv
e
ac
c
u
r
ac
y
[
11
].
2
.
2
.
2
.
M
o
del
w
o
rk
f
lo
w
T
h
e
p
r
e
d
ictiv
e
s
y
s
tem
f
o
llo
ws a
s
tr
u
ctu
r
ed
wo
r
k
f
lo
w:
−
Data
in
p
u
t:
in
g
ests
h
is
to
r
ical
an
d
r
ea
l
-
tim
e
wate
r
d
is
tr
ib
u
tio
n
d
ata,
in
clu
d
in
g
d
ate,
ti
m
e,
ar
ea
,
f
lo
w,
p
r
ess
u
r
e,
an
d
c
o
n
s
u
m
p
tio
n
[
12
].
−
Featu
r
e
en
g
in
ee
r
i
n
g
: p
r
o
ce
s
s
es
d
ata
an
d
s
elec
ts
r
elev
an
t f
ea
t
u
r
es to
im
p
r
o
v
e
m
o
d
el
ac
cu
r
a
cy
[
13
].
−
T
r
ain
in
g
:
tr
ain
s
th
e
r
an
d
o
m
f
o
r
est
m
o
d
el
o
n
h
is
to
r
ical
d
at
a
to
lear
n
wate
r
d
em
an
d
p
att
er
n
s
an
d
d
etec
t
an
o
m
alies [
7
]
.
−
Pre
d
ictio
n
an
d
a
n
o
m
aly
d
etec
tio
n
:
p
r
ed
icts
f
u
tu
r
e
wate
r
d
e
m
an
d
an
d
f
lag
s
u
n
u
s
u
al
p
atte
r
n
s
in
d
icativ
e
o
f
leak
s
o
r
in
ef
f
icien
cies [
3
]
,
[
17
].
−
R
ec
o
m
m
en
d
atio
n
g
e
n
er
atio
n
:
g
en
er
ates
co
n
tr
o
l
co
m
m
an
d
s
f
o
r
au
to
m
ate
d
v
alv
e
o
p
er
ati
o
n
s
to
o
p
tim
ize
d
is
tr
ib
u
tio
n
in
r
ea
l tim
e
[
13
]
,
[
15
].
2
.
3
.
Sy
s
t
e
m
i
m
plem
ent
a
t
io
n
2
.
3
.
1
.
M
o
del
t
ra
ini
ng
a
nd
deplo
y
m
ent
T
h
e
m
o
d
el
is
d
ev
elo
p
ed
a
n
d
i
n
itially
tr
ain
ed
u
s
in
g
Py
th
o
n
l
ib
r
ar
ies
in
clu
d
in
g
Pan
d
as,
Sci
k
it
-
lear
n
,
an
d
Ma
tp
lo
tlib
f
o
r
d
ata
h
a
n
d
li
n
g
,
an
aly
s
is
,
an
d
v
is
u
aliza
tio
n
[
13
]
.
T
h
e
tr
ai
n
ed
m
o
d
el
is
s
er
ialized
u
s
in
g
J
o
b
lib
f
o
r
d
e
p
l
o
y
m
en
t.
T
h
e
d
e
p
lo
y
m
en
t
en
v
ir
o
n
m
en
t
co
n
s
is
ts
o
f
a
s
er
v
er
h
o
s
tin
g
an
ASP.NE
T
web
ap
p
licatio
n
,
wh
ich
in
ter
f
ac
es
with
th
e
C
ity
o
f
Har
ar
e’
s
o
p
er
atio
n
al
d
atab
ases
to
r
etr
iev
e
r
ea
l
-
tim
e
d
ata
f
r
o
m
v
ar
i
o
u
s
lo
ca
tio
n
s
[
12
]
,
[
13
]
.
C
o
m
m
u
n
icatio
n
b
etwe
en
th
e
p
r
ed
ictiv
e
m
o
d
el
an
d
th
e
web
ap
p
lica
tio
n
is
h
an
d
led
v
ia
a
n
am
ed
p
ip
es m
ec
h
an
is
m
,
p
r
o
v
id
in
g
s
ec
u
r
e
an
d
ef
f
icien
t in
ter
p
r
o
ce
s
s
co
m
m
u
n
icatio
n
.
2
.
3
.
2
.
Rea
l
-
t
i
m
e
co
ntr
o
l
a
nd
a
uto
m
a
t
io
n
B
ased
o
n
m
o
d
el
p
r
ed
ictio
n
s
,
t
h
e
web
ap
p
licatio
n
s
en
d
s
c
o
n
t
r
o
l
co
m
m
an
d
s
v
ia
HT
T
PS
o
v
e
r
W
i
-
Fi
to
u
ltra
s
o
n
ic
s
en
s
o
r
s
in
s
talled
t
h
r
o
u
g
h
o
u
t
th
e
wate
r
n
etwo
r
k
.
T
h
ese
s
en
s
o
r
s
au
to
m
ate
v
alv
e
o
p
er
atio
n
s
to
d
y
n
am
ically
a
d
ju
s
t w
ater
f
lo
w
,
im
p
r
o
v
in
g
d
is
tr
ib
u
tio
n
ef
f
icien
cy
an
d
eq
u
ity
[
12
]
,
[
13
]
,
[
15
].
2
.
3
.
3
.
Repro
du
cibili
t
y
All
s
tep
s
,
f
r
o
m
d
ata
l
o
ad
in
g
to
m
o
d
el
tr
ain
in
g
,
v
alid
atio
n
,
an
d
d
ep
l
o
y
m
en
t,
ar
e
f
u
lly
d
o
cu
m
en
ted
an
d
ac
ce
s
s
ib
le
v
ia
a
Go
o
g
le
C
o
lab
n
o
te
b
o
o
k
.
T
h
is
en
s
u
r
es
th
at
o
th
er
r
esear
ch
er
s
a
n
d
p
r
ac
titi
o
n
er
s
ca
n
r
ep
r
o
d
u
ce
th
e
r
esu
lts
u
s
in
g
th
e
s
am
e
d
ataset
an
d
alg
o
r
ith
m
s
[
1
2
]
,
[
13
].
2
.
4
.
M
o
del
e
v
a
lua
t
i
o
n
T
h
e
p
r
ed
ictiv
e
m
o
d
el’
s
p
er
f
o
r
m
an
ce
was
ev
alu
ated
u
s
in
g
a
co
m
b
i
n
atio
n
o
f
r
eg
r
e
s
s
io
n
an
d
class
if
icatio
n
m
etr
ics
.
Fig
u
r
e
2
p
r
esen
ts
th
e
p
er
f
o
r
m
an
ce
m
etr
ics
u
s
ed
f
o
r
ev
alu
at
in
g
wate
r
d
em
a
n
d
f
o
r
ec
asti
n
g
an
d
an
o
m
aly
d
etec
tio
n
.
Fig
u
r
e
2
.
Mo
d
el
ev
alu
atio
n
r
e
s
u
lts
: p
er
f
o
r
m
an
ce
m
et
r
ics f
o
r
d
em
an
d
f
o
r
ec
asti
n
g
a
n
d
an
o
m
aly
d
etec
tio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
7
2
2
-
3
2
2
1
C
o
m
p
u
t Sci
I
n
f
T
ec
h
n
o
l
,
Vo
l.
7
,
No
.
2
,
J
u
ly
20
26
:
231
-
2
4
0
234
2
.
4
.
1
.
Reg
re
s
s
io
n
m
et
rics
Fig
u
r
e
3
p
r
es
e
n
ts
t
h
e
r
e
g
r
ess
i
o
n
m
et
r
ics
u
s
e
d
t
o
e
v
al
u
at
e
t
h
e
p
r
e
d
ic
ti
v
e
m
o
d
e
l.
T
h
ese
m
e
tr
i
cs m
e
as
u
r
e
p
r
e
d
ic
ti
o
n
ac
cu
r
a
c
y
a
n
d
e
r
r
o
r
m
a
g
n
it
u
d
e
,
wit
h
l
o
w
er
m
ea
n
a
b
s
o
lu
te
er
r
o
r
(
MA
E
)
,
m
ea
n
s
q
u
ar
ed
er
r
o
r
(
MSE
)
,
an
d
r
o
o
t
m
ea
n
s
q
u
ar
e
d
er
r
o
r
(
R
MSE
)
v
al
u
es
i
n
d
i
ca
t
in
g
b
e
tt
er
p
e
r
f
o
r
m
a
n
c
e,
w
h
ile
a
h
i
g
h
e
r
R
²
s
co
r
e
r
ef
le
cts
a
s
tr
o
n
g
er
f
it
b
etw
ee
n
p
r
e
d
i
cte
d
an
d
ac
tu
al
wa
te
r
d
e
m
a
n
d
v
al
u
e
s
.
−
MA
E
:
a
v
er
ag
e
ab
s
o
lu
te
d
if
f
e
r
en
ce
b
etwe
en
p
r
e
d
icted
a
n
d
ac
tu
al
v
al
u
es;
lo
wer
v
al
u
es
in
d
icate
h
ig
h
er
ac
cu
r
ac
y
.
−
MSE
:
a
v
er
a
g
e
s
q
u
ar
e
d
d
if
f
e
r
e
n
ce
; p
en
alize
s
lar
g
er
e
r
r
o
r
s
.
−
R
MSE
:
s
q
u
ar
e
r
o
o
t o
f
MSE
; i
n
th
e
s
am
e
u
n
its
as th
e
tar
g
et
v
ar
iab
le.
−
R
²
s
co
r
e:
p
r
o
p
o
r
tio
n
o
f
v
ar
ia
n
ce
in
ac
tu
al
d
em
a
n
d
e
x
p
lain
ed
b
y
th
e
m
o
d
el;
v
al
u
es
clo
s
er
to
1
in
d
icate
b
etter
f
it [
5
]
,
[
10
].
Fig
u
r
e
3
.
R
eg
r
ess
io
n
m
etr
ics i
m
p
lem
en
ted
2
.
4
.
2
.
Cla
s
s
if
ica
t
io
n
m
et
rics (
a
no
m
a
ly
det
ec
t
io
n)
T
h
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
an
o
m
aly
d
etec
tio
n
m
o
d
el
was
ass
es
s
ed
u
s
in
g
s
tan
d
a
r
d
class
if
icatio
n
m
etr
ics.
T
h
ese
m
etr
ics
ev
alu
ate
th
e
m
o
d
el
’
s
ca
p
ab
ilit
y
to
ac
cu
r
atel
y
d
is
tin
g
u
is
h
an
o
m
alo
u
s
co
n
d
itio
n
s
f
r
o
m
n
o
r
m
al
o
p
er
atin
g
s
tates.
T
h
e
m
etr
ics u
s
ed
in
th
is
s
tu
d
y
ar
e:
−
Acc
u
r
ac
y
:
p
r
o
p
o
r
tio
n
o
f
co
r
r
e
ct
an
o
m
aly
d
etec
tio
n
s
.
−
Pre
cisi
o
n
:
p
r
o
p
o
r
tio
n
o
f
tr
u
e
p
o
s
itiv
es a
m
o
n
g
p
r
e
d
icted
p
o
s
itiv
es.
−
R
ec
all:
p
r
o
p
o
r
tio
n
o
f
tr
u
e
p
o
s
i
tiv
es id
en
tifie
d
am
o
n
g
ac
tu
al
p
o
s
itiv
es.
−
F1
s
co
r
e:
h
ar
m
o
n
ic
m
ea
n
o
f
p
r
ec
is
io
n
an
d
r
ec
all
[
1
]
,
[
3
]
,
[
17
].
2
.
4
.
3
.
F
ea
t
ure
i
m
po
rt
a
nce
a
n
d v
is
ua
liza
t
io
n
Featu
r
e
im
p
o
r
tan
ce
was
ex
tr
ac
ted
f
r
o
m
th
e
r
an
d
o
m
f
o
r
est
m
o
d
el
to
id
en
tify
k
e
y
f
ac
to
r
s
af
f
ec
tin
g
wate
r
d
em
an
d
.
Vis
u
aliza
tio
n
tech
n
iq
u
es
,
in
clu
d
in
g
r
ec
eiv
er
o
p
er
atin
g
ch
ar
ac
ter
is
tic
(
R
OC
)
cu
r
v
es,
p
er
f
o
r
m
an
ce
v
er
s
u
s
tr
ain
in
g
s
i
ze
,
an
d
h
ea
tm
ap
s
,
wer
e
u
s
ed
t
o
s
u
p
p
o
r
t
in
ter
p
r
etab
ilit
y
an
d
o
p
er
atio
n
al
i
n
s
ig
h
ts
[
13
].
F
ig
u
r
e
4
illu
s
tr
ates
th
e
r
elativ
e
im
p
o
r
tan
ce
o
f
th
e
i
n
p
u
t
f
ea
tu
r
es
u
s
ed
b
y
th
e
m
o
d
el
i
n
p
r
e
d
ictin
g
wate
r
d
em
an
d
a
n
d
d
etec
tin
g
a
n
o
m
al
ies.
T
h
e
an
aly
s
is
h
ig
h
lig
h
ts
th
e
v
ar
iab
les
th
at
c
o
n
tr
ib
u
te
m
o
s
t
s
ig
n
if
ican
tly
to
th
e
m
o
d
el
’
s
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
,
p
r
o
v
id
i
n
g
v
al
u
ab
le
in
s
ig
h
ts
in
to
th
e
f
ac
to
r
s
in
f
lu
en
cin
g
wate
r
d
is
tr
ib
u
tio
n
p
er
f
o
r
m
an
ce
.
Fig
u
r
e
5
p
r
esen
ts
th
e
r
elatio
n
s
h
ip
b
etwe
en
m
o
d
el
p
er
f
o
r
m
an
ce
an
d
tr
ain
in
g
d
ataset
s
ize.
T
h
e
f
ig
u
r
e
d
em
o
n
s
tr
ates
h
o
w
th
e
p
r
ed
ictiv
e
ca
p
ab
il
ity
o
f
t
h
e
m
o
d
el
e
v
o
lv
es
as
m
o
r
e
tr
a
in
i
n
g
d
ata
b
ec
o
m
e
av
ailab
le,
h
elp
i
n
g
to
ass
es
s
m
o
d
el
s
ca
lab
ilit
y
,
le
ar
n
in
g
b
e
h
av
io
r
,
an
d
d
ata
s
u
f
f
icien
cy
.
Fig
u
r
e
6
s
h
o
ws
th
e
R
OC
cu
r
v
e
th
e
tr
ad
e
-
o
f
f
b
etwe
en
tr
u
e
p
o
s
itiv
e
r
ate
an
d
f
alse
p
o
s
itiv
e
r
ate
f
o
r
ea
ch
p
r
io
r
it
y
class
.
I
t
h
ig
h
lig
h
ts
th
e
m
o
d
el
’
s
ab
i
lity
to
d
is
cr
im
in
ate
b
etwe
en
n
o
r
m
al
an
d
an
o
m
alo
u
s
wate
r
d
is
tr
ib
u
tio
n
ev
en
ts
,
with
h
ig
h
er
a
r
ea
s
u
n
d
e
r
th
e
cu
r
v
e
in
d
icatin
g
b
etter
class
if
icatio
n
p
er
f
o
r
m
an
ce
.
Evaluation Warning : The document was created with Spire.PDF for Python.
C
o
m
p
u
t Sci
I
n
f
T
ec
h
n
o
l
I
SS
N:
2722
-
3
2
2
1
Op
timiz
in
g
w
a
ter d
is
t
r
ib
u
tio
n
in
Ha
r
a
r
e,
Zimb
a
b
w
e
u
s
in
g
I
o
T a
n
d
clo
u
d
co
mp
u
tin
g
(
A
n
g
el
in
e
Ts
a
ts
a
)
235
Fig
u
r
e
4
.
R
elativ
e
im
p
o
r
tan
ce
o
f
in
p
u
t f
ea
tu
r
es in
p
r
ed
ictin
g
wate
r
d
em
an
d
a
n
d
d
etec
tin
g
a
n
o
m
ali
es
Fig
u
r
e
5
.
Mo
d
el
p
er
f
o
r
m
a
n
ce
m
etr
ics as a
f
u
n
ctio
n
o
f
tr
ain
in
g
d
ataset
s
ize
Fig
u
r
e
6
.
R
OC
cu
r
v
e
illu
s
tr
atin
g
m
o
d
el
p
er
f
o
r
m
a
n
ce
ac
r
o
s
s
p
r
io
r
ity
class
es f
o
r
an
o
m
aly
d
etec
tio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
7
2
2
-
3
2
2
1
C
o
m
p
u
t Sci
I
n
f
T
ec
h
n
o
l
,
Vo
l.
7
,
No
.
2
,
J
u
ly
20
26
:
231
-
2
4
0
236
2
.
5
.
I
nte
g
ra
t
io
n wit
h Io
T
a
nd
clo
ud
pla
t
f
o
rm
s
T
h
e
f
u
lly
tr
ain
ed
m
o
d
el
is
in
t
eg
r
ated
with
I
o
T
s
en
s
o
r
s
an
d
clo
u
d
in
f
r
astru
ctu
r
e
t
o
en
a
b
le
r
ea
l
-
tim
e,
au
to
m
ated
wate
r
m
a
n
ag
em
en
t
.
T
h
e
s
y
s
tem
s
u
p
p
o
r
ts
s
ca
lab
le
,
s
ec
u
r
e
d
ep
lo
y
m
en
t,
an
d
c
o
n
t
in
u
o
u
s
m
o
n
ito
r
i
n
g
,
allo
win
g
f
o
r
r
ap
id
r
esp
o
n
s
e
to
an
o
m
alies a
n
d
im
p
r
o
v
e
d
o
p
er
atio
n
al
ef
f
icien
cy
[
13
]
,
[
15
]
,
[
19
]
, [
20
]
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
Wa
t
er
dem
a
nd
f
o
re
c
a
s
t
ing
3
.
1
.
1
.
Reg
re
s
s
io
n
m
et
rics
T
h
e
tr
ain
ed
r
an
d
o
m
f
o
r
est
m
o
d
el’
s
p
r
ed
ictiv
e
p
er
f
o
r
m
a
n
ce
was
ev
alu
ated
o
n
th
e
test
d
a
taset.
Key
r
eg
r
ess
io
n
m
etr
ics
ar
e
s
u
m
m
a
r
ized
in
T
ab
le
1
.
T
h
e
h
ig
h
R
²
s
co
r
e
o
f
0
.
8
9
in
d
icate
s
th
at
t
h
e
m
o
d
el
e
x
p
lain
s
m
o
s
t
o
f
th
e
v
ar
iab
ilit
y
in
w
ater
d
em
an
d
,
r
ef
lectin
g
s
tr
o
n
g
p
r
ed
ictiv
e
ac
cu
r
ac
y
.
L
o
w
MA
E
an
d
R
MSE
d
em
o
n
s
tr
ate
p
r
ec
is
e
d
em
an
d
esti
m
atio
n
ac
r
o
s
s
d
if
f
er
en
t
zo
n
es.
T
h
is
im
p
lies
th
e
m
o
d
el
ca
n
r
eliab
ly
s
u
p
p
o
r
t
o
p
er
atio
n
al
p
lan
n
in
g
,
leak
d
et
ec
tio
n
,
an
d
eq
u
itab
le
d
is
tr
ib
u
tio
n
[
5
]
,
[
10
].
T
ab
le
1
.
R
eg
r
ess
io
n
m
etr
ics f
o
r
wate
r
d
em
an
d
p
r
e
d
ictio
n
M
e
t
r
i
c
V
a
l
u
e
M
A
E
0
.
1
2
8
M
S
E
0
.
0
3
1
R
M
S
E
0
.
1
7
6
R
²
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c
o
r
e
0
.
8
9
3
.
1
.
2
.
P
re
dict
ed
v
s
.
Act
ua
l
dem
a
nd
Fig
u
r
e
7
illu
s
tr
ates
th
e
p
r
ed
ict
ed
v
er
s
u
s
ac
tu
al
wate
r
d
em
an
d
o
v
er
a
r
e
p
r
esen
tativ
e
wee
k
.
Peak
s
an
d
tr
o
u
g
h
s
ar
e
well
-
ca
p
tu
r
ed
,
s
h
o
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g
th
e
m
o
d
el’
s
ab
ilit
y
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f
o
llo
w
d
aily
a
n
d
wee
k
ly
c
o
n
s
u
m
p
tio
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p
atter
n
s
.
Min
o
r
d
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iatio
n
s
o
cc
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r
d
u
r
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g
s
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d
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e
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m
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ig
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g
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en
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u
s
in
g
ad
v
a
n
ce
d
tim
e
-
s
er
ies m
o
d
els [
10
].
Fig
u
r
e
7
.
P
r
ed
ict
ed
v
e
r
s
u
s
ac
t
u
al
wat
er
d
e
m
a
n
d
o
v
e
r
a
r
e
p
r
es
en
t
ati
v
e
w
ee
k
i
n
H
ar
a
r
e
,
h
i
g
h
li
g
h
ti
n
g
t
h
e
m
o
d
el’
s
a
b
i
lit
y
t
o
ca
p
t
u
r
e
d
ail
y
a
n
d
we
e
k
l
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n
s
u
m
p
ti
o
n
p
at
ter
n
s
wit
h
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in
o
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ev
iati
o
n
s
d
u
r
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n
g
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u
d
d
en
s
p
i
k
es
3
.
2
.
Ano
m
a
ly
d
et
ec
t
i
o
n
3
.
2
.
1
.
Cla
s
s
if
ica
t
io
n
m
et
rics
T
h
e
m
o
d
el’
s
ab
ilit
y
to
d
ete
ct
ir
r
eg
u
lar
wate
r
u
s
ag
e
o
r
leak
s
was
ev
alu
ated
u
s
in
g
s
tan
d
ar
d
class
if
icatio
n
m
etr
ics,
as
s
h
o
w
n
in
T
ab
le
2
.
T
h
e
r
esu
lts
in
d
ic
ate
th
at
h
ig
h
r
ec
all
en
a
b
les
ef
f
ec
tiv
e
d
etec
tio
n
o
f
tr
u
e
an
o
m
alies,
en
s
u
r
in
g
th
at
leak
s
o
r
u
n
u
s
u
al
c
o
n
s
u
m
p
ti
o
n
p
atter
n
s
ar
e
u
n
lik
ely
to
g
o
u
n
n
o
ticed
.
Hig
h
p
r
ec
is
io
n
m
i
n
im
izes
f
alse
al
ar
m
s
,
p
r
e
v
en
tin
g
u
n
n
ec
ess
ar
y
o
p
er
atio
n
al
in
ter
v
e
n
tio
n
s
.
Ov
er
all,
th
e
m
o
d
el
p
r
o
v
id
es a
r
eliab
le
to
o
l f
o
r
r
ea
l
-
tim
e
an
o
m
aly
d
etec
tio
n
in
u
r
b
an
wate
r
n
etwo
r
k
s
[
3
]
,
[
7
]
,
[
17
].
Evaluation Warning : The document was created with Spire.PDF for Python.
C
o
m
p
u
t Sci
I
n
f
T
ec
h
n
o
l
I
SS
N:
2722
-
3
2
2
1
Op
timiz
in
g
w
a
ter d
is
t
r
ib
u
tio
n
in
Ha
r
a
r
e,
Zimb
a
b
w
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u
s
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g
I
o
T a
n
d
clo
u
d
co
mp
u
tin
g
(
A
n
g
el
in
e
Ts
a
ts
a
)
237
T
ab
le
2
.
An
o
m
aly
d
etec
tio
n
p
er
f
o
r
m
a
n
ce
M
e
t
r
i
c
V
a
l
u
e
(
%)
A
c
c
u
r
a
c
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r
e
c
i
s
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o
n
91
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e
c
a
l
l
92
F1
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c
o
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e
91
3
.
2
.
2
.
Co
nfusi
o
n
m
a
t
rix
a
nd
RO
C
c
urv
e
Fig
u
r
e
8
s
h
o
ws
th
e
co
n
f
u
s
io
n
m
atr
ix
f
o
r
an
o
m
aly
d
etec
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illu
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tr
atin
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h
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ala
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b
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tr
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p
o
s
itiv
e
an
d
f
alse
p
o
s
itiv
e
p
r
ed
ictio
n
s
.
Fig
u
r
e
6
p
r
esen
ts
th
e
R
OC
cu
r
v
e,
co
n
f
ir
m
in
g
s
t
r
o
n
g
d
is
cr
im
in
ativ
e
ca
p
ab
ilit
y
o
f
th
e
m
o
d
el.
Fig
u
r
e
8
.
C
o
n
f
u
s
io
n
m
atr
i
x
f
o
r
an
o
m
aly
d
etec
tio
n
s
h
o
win
g
t
h
e
d
is
tr
ib
u
tio
n
o
f
tr
u
e
p
o
s
itiv
e
s
,
tr
u
e
n
eg
ativ
es,
f
alse p
o
s
itiv
es,
an
d
f
alse n
eg
at
iv
es
3
.
3
.
F
e
a
t
ure
im
po
rt
a
nce
a
na
l
y
s
is
T
h
e
r
an
d
o
m
f
o
r
est
m
o
d
el
i
d
en
tifie
d
k
ey
p
r
ed
icto
r
s
in
f
l
u
en
ci
n
g
wate
r
d
em
a
n
d
a
n
d
an
o
m
ali
es:
−
His
to
r
ical
co
n
s
u
m
p
tio
n
tr
e
n
d
s
−
T
im
e
-
of
-
d
ay
an
d
d
a
y
-
of
-
week
−
Pre
s
s
u
r
e
m
ea
s
u
r
em
en
ts
Dis
cu
s
s
io
n
:
p
r
io
r
itizin
g
th
ese
f
ea
tu
r
es
allo
ws
city
o
p
er
ato
r
s
to
f
o
cu
s
m
o
n
ito
r
in
g
a
n
d
co
n
tr
o
l
ef
f
o
r
ts
o
n
th
e
m
o
s
t
in
f
lu
en
tial
f
ac
to
r
s
.
T
em
p
o
r
al
an
d
p
r
ess
u
r
e
-
r
elate
d
f
ea
tu
r
es
d
i
r
ec
tly
in
f
o
r
m
v
alv
e
au
to
m
atio
n
d
ec
is
io
n
s
an
d
leak
d
etec
tio
n
p
r
o
to
co
ls
,
s
u
p
p
o
r
tin
g
d
ata
-
d
r
iv
e
n
r
eso
u
r
c
e
m
an
ag
em
e
n
t [
13
].
3
.
4
.
O
pera
t
io
na
l
im
pa
c
t
o
f
r
ea
l
-
t
im
e
deplo
y
m
ent
Fig
u
r
e
9
p
r
esen
ts
d
ash
b
o
ar
d
o
u
tp
u
ts
f
o
r
r
ea
l
-
tim
e
wate
r
d
is
tr
ib
u
tio
n
m
o
n
ito
r
in
g
,
i
n
clu
d
in
g
wate
r
f
lo
w,
p
r
ess
u
r
e,
a
n
d
d
etec
ted
a
n
o
m
alies.
Fig
u
r
e
9
.
O
u
t
p
u
ts
f
o
r
r
ea
l
-
tim
e
wate
r
d
is
tr
ib
u
tio
n
m
o
n
ito
r
in
g
,
in
clu
d
i
n
g
wate
r
f
l
o
w,
p
r
ess
u
r
e,
an
d
d
etec
ted
an
o
m
alies
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
7
2
2
-
3
2
2
1
C
o
m
p
u
t Sci
I
n
f
T
ec
h
n
o
l
,
Vo
l.
7
,
No
.
2
,
J
u
ly
20
26
:
231
-
2
4
0
238
T
h
e
d
ep
lo
y
m
en
t
r
esu
lts
in
d
icate
m
ea
s
u
r
ab
le
b
en
e
f
its
:
−
W
ater
lo
s
s
r
ed
u
ctio
n
ac
r
o
s
s
m
o
n
ito
r
ed
z
o
n
es.
−
I
m
p
r
o
v
ed
e
q
u
ity
o
f
s
u
p
p
ly
b
et
wee
n
ar
ea
s
.
−
R
ap
id
r
esp
o
n
s
e
to
d
etec
ted
an
o
m
alies v
ia
au
to
m
ated
v
alv
e
c
o
n
tr
o
l.
T
h
e
r
esu
lts
co
n
f
ir
m
t
h
at
co
m
b
in
in
g
I
o
T
d
ata,
ML
,
a
n
d
au
t
o
m
ated
co
n
tr
o
l
s
u
p
p
o
r
ts
s
u
s
tain
ab
le
u
r
b
a
n
wate
r
m
an
ag
em
e
n
t
in
Har
ar
e,
ev
en
u
n
d
er
co
n
s
tr
ain
ts
lik
e
ag
in
g
in
f
r
astru
ctu
r
e
an
d
v
ar
ia
b
le
co
n
s
u
m
p
tio
n
[
12
]
,
[
13
]
,
[
15
]
,
[
2
1
]
.
T
h
e
s
tu
d
y
d
e
m
o
n
s
tr
ates
th
at
ad
v
an
ce
d
an
a
ly
tics
,
I
o
T
s
en
s
in
g
,
an
d
clo
u
d
-
b
ased
m
o
n
ito
r
i
n
g
ca
n
p
r
o
v
id
e
p
r
ac
tical
an
d
s
ca
l
ab
le
s
o
lu
tio
n
s
f
o
r
r
eso
u
r
ce
-
co
n
s
tr
ain
ed
u
r
b
an
wate
r
s
y
s
tem
s
.
Similar
f
in
d
in
g
s
h
av
e
b
ee
n
r
ep
o
r
ted
in
p
r
e
v
io
u
s
s
tu
d
ies
o
n
s
m
ar
t
wate
r
m
an
ag
em
en
t,
h
ig
h
lig
h
tin
g
t
h
e
p
o
t
en
tial
o
f
in
tellig
en
t
m
o
n
ito
r
in
g
an
d
a
u
to
m
ated
c
o
n
tr
o
l
tech
n
o
lo
g
ies
to
im
p
r
o
v
e
o
p
er
atio
n
al
e
f
f
icien
cy
a
n
d
s
e
r
v
ice
r
eliab
ilit
y
in
de
v
elo
p
in
g
u
r
b
an
e
n
v
ir
o
n
m
en
t
s
[
15
]
,
[
16
]
,
[
22
].
3
.
5
.
L
im
it
a
t
io
ns
a
nd
f
uture
direct
io
ns
Desp
ite
th
e
ef
f
ec
tiv
en
ess
o
f
t
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
,
s
ev
er
a
l
lim
itatio
n
s
an
d
o
p
p
o
r
tu
n
ities
f
o
r
f
u
tu
r
e
im
p
r
o
v
em
e
n
t h
av
e
b
ee
n
i
d
en
ti
f
ied
.
−
T
h
e
m
ain
lim
itatio
n
s
in
clu
d
e
s
en
s
o
r
n
etwo
r
k
r
eliab
ilit
y
is
s
u
es a
n
d
o
cc
asio
n
al
d
ata
laten
c
y
.
−
Fu
tu
r
e
wo
r
k
:
i
n
teg
r
atio
n
o
f
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
f
o
r
ad
v
an
ce
d
tim
e
-
s
er
ies
f
o
r
ec
asti
n
g
,
ex
p
an
d
e
d
s
en
s
o
r
co
v
er
ag
e
,
an
d
en
h
a
n
ce
d
cy
b
er
s
ec
u
r
ity
[
10
]
,
[
23
].
Fu
tu
r
e
r
esear
ch
will
f
o
c
u
s
o
n
ex
ten
d
in
g
p
r
ed
ictiv
e
ca
p
ab
ilit
ies
u
s
in
g
L
STM
m
o
d
els
f
o
r
im
p
r
o
v
e
d
tim
e
-
s
er
ies
f
o
r
ec
asti
n
g
,
ex
p
a
n
d
in
g
s
en
s
o
r
co
v
e
r
ag
e
f
o
r
f
i
n
er
-
g
r
ai
n
ed
c
o
n
tr
o
l,
co
n
d
u
cti
n
g
s
o
cio
-
ec
o
n
o
m
ic
ad
o
p
tio
n
s
tu
d
ies,
an
d
en
h
an
c
in
g
cy
b
er
s
ec
u
r
ity
m
ec
h
an
is
m
s
f
o
r
r
o
b
u
s
t
m
u
n
icip
al
d
ep
l
o
y
m
en
t
[
16
]
,
[
23
].
Fu
r
th
er
m
o
r
e
,
th
e
co
n
tin
u
ed
d
e
v
elo
p
m
en
t
o
f
s
m
ar
t
wate
r
m
a
n
ag
em
en
t
tech
n
o
lo
g
ies,
s
u
p
p
o
r
ted
b
y
a
d
v
an
ce
s
in
ar
tific
ial
in
tellig
en
ce
,
I
o
T
in
f
r
astru
ctu
r
e,
an
d
s
m
ar
t
-
city
in
itiativ
es,
m
ay
f
u
r
t
h
er
en
h
an
ce
th
e
s
u
s
tain
ab
ilit
y
an
d
r
esil
ien
ce
o
f
u
r
b
an
wate
r
d
is
tr
i
b
u
tio
n
s
y
s
tem
s
in
d
ev
el
o
p
in
g
r
eg
io
n
s
[
24
]
,
[
25
].
4.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
ad
d
r
ess
ed
in
ef
f
ici
en
cies
in
Har
ar
e’
s
u
r
b
an
wa
ter
d
is
tr
ib
u
tio
n
s
y
s
tem
b
y
d
ev
elo
p
in
g
a
d
ata
-
d
r
iv
en
o
p
tim
izatio
n
f
r
am
ewo
r
k
th
at
in
teg
r
ates
I
o
T
s
en
s
in
g
,
r
an
d
o
m
f
o
r
es
t
ML
,
an
d
clo
u
d
-
b
ase
d
ASP.NE
T
d
ep
lo
y
m
e
n
t.
T
h
e
p
r
im
ar
y
o
b
jectiv
es
wer
e
to
ac
c
u
r
ately
f
o
r
ec
ast
wate
r
d
em
an
d
,
d
etec
t
an
o
m
alies
s
u
ch
as
leak
s
,
an
d
o
p
tim
ize
v
alv
e
co
n
tr
o
l
ac
r
o
s
s
d
if
f
er
en
t
c
ity
zo
n
es.
T
h
e
r
esu
lts
p
r
esen
t
ed
in
t
h
e
r
esu
lts
an
d
d
is
cu
s
s
io
n
s
ec
tio
n
co
n
f
ir
m
th
at
th
e
p
r
o
p
o
s
ed
s
y
s
tem
ac
h
ie
v
es
th
ese
o
b
jectiv
es.
T
h
e
r
an
d
o
m
f
o
r
est
m
o
d
el
ac
h
iev
ed
an
R
²
s
co
r
e
o
f
0
.
8
9
f
o
r
wate
r
d
em
a
n
d
f
o
r
ec
asti
n
g
,
wh
ile
an
o
m
aly
d
etec
tio
n
ac
h
ie
v
ed
9
4
%
ac
cu
r
ac
y
,
9
1
% p
r
ec
is
io
n
,
9
2
% r
ec
all
,
an
d
an
F1
-
s
co
r
e
o
f
9
1
%
.
R
ea
l
-
tim
e
d
ep
lo
y
m
en
t
en
ab
le
d
au
to
m
ated
v
alv
e
co
n
tr
o
l
an
d
im
p
r
o
v
e
d
eq
u
itab
le
wate
r
d
is
tr
ib
u
tio
n
ac
r
o
s
s
Har
ar
e,
d
em
o
n
s
tr
atin
g
th
e
o
p
er
atio
n
al
b
en
ef
its
o
f
in
teg
r
atin
g
I
o
T
an
d
clo
u
d
-
b
a
s
ed
an
aly
tics
.
T
h
e
f
in
d
in
g
s
d
em
o
n
s
tr
ate
th
at
a
d
v
a
n
ce
d
a
n
aly
tics
an
d
I
o
T
in
teg
r
atio
n
ca
n
p
r
o
v
id
e
p
r
ac
tical
s
o
lu
tio
n
s
f
o
r
r
eso
u
r
ce
-
co
n
s
tr
ain
ed
u
r
b
a
n
e
n
v
ir
o
n
m
e
n
ts
.
T
h
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
p
r
o
v
id
es
a
f
o
u
n
d
atio
n
f
o
r
f
u
tu
r
e
s
m
ar
t
wate
r
m
an
ag
em
en
t
in
itiativ
es
in
d
e
v
elo
p
in
g
u
r
b
an
e
n
v
ir
o
n
m
en
ts
an
d
s
u
p
p
o
r
ts
b
r
o
a
d
er
d
ig
ital
tr
an
s
f
o
r
m
atio
n
an
d
s
u
s
tain
ab
ilit
y
o
b
jectiv
es.
ACK
NO
WL
E
DG
M
E
N
T
S
T
h
e
au
th
o
r
s
wis
h
to
ac
k
n
o
wled
g
e
th
e
C
ity
o
f
Har
ar
e
W
ater
Dep
ar
tm
en
t
f
o
r
p
r
o
v
id
in
g
h
is
to
r
ical
wate
r
d
is
tr
ib
u
tio
n
d
ata
an
d
t
ec
h
n
ical
g
u
i
d
an
ce
.
W
e
also
th
an
k
r
esear
ch
ass
is
tan
ts
at
Har
ar
e
I
n
s
titu
te
o
f
T
ec
h
n
o
lo
g
y
f
o
r
t
h
eir
s
u
p
p
o
r
t
in
d
ata
p
r
ep
r
o
ce
s
s
in
g
an
d
s
e
n
s
o
r
d
e
p
lo
y
m
e
n
t.
All
in
d
iv
i
d
u
als
ac
k
n
o
wled
g
e
d
h
av
e
p
r
o
v
id
e
d
co
n
s
en
t t
o
b
e
li
s
ted
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
is
r
esear
ch
was
s
u
p
p
o
r
ted
b
y
An
g
elin
e
T
s
atsa
,
th
e
C
ity
o
f
Har
ar
e,
T
o
wn
C
ler
k
’
s
Dep
ar
tm
en
t,
I
T
Div
is
io
n
,
an
d
th
e
Har
a
r
e
I
n
s
t
itu
te
o
f
T
ec
h
n
o
lo
g
y
.
T
h
e
a
u
t
h
o
r
s
g
r
atef
u
lly
ac
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n
o
wled
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e
th
eir
f
in
an
cial
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d
in
s
titu
tio
n
al
s
u
p
p
o
r
t,
wh
ich
e
n
ab
led
th
e
ac
q
u
is
itio
n
o
f
d
at
a,
s
en
s
o
r
d
ep
lo
y
m
en
t,
a
n
d
d
ev
elo
p
m
en
t
o
f
th
e
p
r
ed
ictiv
e
wate
r
d
is
tr
ib
u
tio
n
m
o
d
el.
Evaluation Warning : The document was created with Spire.PDF for Python.
C
o
m
p
u
t Sci
I
n
f
T
ec
h
n
o
l
I
SS
N:
2722
-
3
2
2
1
Op
timiz
in
g
w
a
ter d
is
t
r
ib
u
tio
n
in
Ha
r
a
r
e,
Zimb
a
b
w
e
u
s
in
g
I
o
T a
n
d
clo
u
d
co
mp
u
tin
g
(
A
n
g
el
in
e
Ts
a
ts
a
)
239
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
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th
e
C
o
n
t
r
ib
u
to
r
R
o
les
T
a
x
o
n
o
m
y
(
C
R
ed
iT)
to
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ec
o
g
n
ize
in
d
iv
i
d
u
al
au
th
o
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n
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ib
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tio
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,
r
ed
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th
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r
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h
ip
d
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u
tes,
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d
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llab
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m
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Aut
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Vi
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Fu
An
g
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s
atsa
✓
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T
in
ash
e
B
u
ts
a
✓
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Yo
lan
d
a
C
h
ib
ay
a
✓
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✓
✓
✓
✓
✓
✓
✓
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
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d
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y
So
:
So
f
t
w
a
r
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Va
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Va
l
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d
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t
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mal
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r
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Fu
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n
d
i
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g
a
c
q
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si
t
i
o
n
CO
NF
L
I
C
T
O
F
I
N
T
E
R
E
S
T
ST
A
T
E
M
E
NT
Au
th
o
r
s
s
tate
n
o
co
n
f
lict o
f
in
t
er
est.
DATA AV
AI
L
AB
I
L
I
T
Y
T
h
e
d
ata
th
at
s
u
p
p
o
r
t
th
e
f
i
n
d
in
g
s
o
f
th
is
s
tu
d
y
ar
e
av
aila
b
le
o
n
r
eq
u
est
f
r
o
m
th
e
co
r
r
esp
o
n
d
in
g
au
th
o
r
,
[
AT
]
.
T
h
e
d
ata
co
n
t
ain
s
en
s
itiv
e
m
u
n
icip
al
wate
r
d
is
tr
ib
u
tio
n
in
f
o
r
m
atio
n
a
n
d
ar
e
n
o
t
p
u
b
licly
av
ailab
le
d
u
e
to
p
r
iv
ac
y
an
d
s
ec
u
r
ity
r
estrictio
n
s
.
RE
F
E
R
E
NC
E
S
[
1
]
M
.
N
.
K
a
n
y
a
ma,
F
.
B
.
S
h
a
v
a
,
A
.
M
.
G
a
m
u
n
d
a
n
i
,
a
n
d
A
.
H
a
r
t
ma
n
n
,
“
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
p
p
l
i
c
a
t
i
o
n
s
f
o
r
a
n
o
mal
y
d
e
t
e
c
t
i
o
n
i
n
S
mart
W
a
t
e
r
M
e
t
e
r
i
n
g
N
e
t
w
o
r
k
s
:
A
s
y
st
e
ma
t
i
c
r
e
v
i
e
w
,
”
Ph
y
si
c
s
a
n
d
C
h
e
m
i
st
ry
o
f
t
h
e
E
a
r
t
h
,
v
o
l
.
1
3
4
,
p
.
1
0
3
5
5
8
,
Ju
n
.
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
p
c
e
.
2
0
2
4
.
1
0
3
5
5
8
.
[
2
]
S
.
L
e
e
a
n
d
B
.
K
i
m,
“
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
m
o
d
e
l
f
o
r
l
e
a
k
d
e
t
e
c
t
i
o
n
u
s
i
n
g
w
a
t
e
r
p
i
p
e
l
i
n
e
v
i
b
r
a
t
i
o
n
se
n
so
r
,
”
S
e
n
so
rs
,
v
o
l
.
2
3
,
n
o
.
2
1
,
p
.
8
9
3
5
,
N
o
v
.
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
s
2
3
2
1
8
9
3
5
.
[
3
]
L.
R
o
m
e
r
o
-
B
e
n
,
D
.
A
l
v
e
s,
J.
B
l
e
sa,
G
.
C
e
mb
r
a
n
o
,
V
.
P
u
i
g
,
a
n
d
E.
D
u
v
i
e
l
l
a
,
“
Le
a
k
d
e
t
e
c
t
i
o
n
a
n
d
l
o
c
a
l
i
z
a
t
i
o
n
i
n
w
a
t
e
r
d
i
st
r
i
b
u
t
i
o
n
n
e
t
w
o
r
k
s:
r
e
v
i
e
w
a
n
d
p
e
r
s
p
e
c
t
i
v
e
,
”
A
n
n
u
a
l
Re
v
i
e
w
s
i
n
C
o
n
t
r
o
l
,
v
o
l
.
5
5
,
p
p
.
3
9
2
–
4
1
9
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
a
r
c
o
n
t
r
o
l
.
2
0
2
3
.
0
3
.
0
1
2
.
[
4
]
S
.
P
r
o
m
p
u
t
,
S
.
M
a
i
t
h
o
mk
l
a
n
g
,
a
n
d
C
.
P
a
n
y
a
-
i
sara
,
“
D
e
si
g
n
a
n
d
a
n
a
l
y
si
s
p
e
r
f
o
r
ma
n
c
e
o
f
I
o
T
-
b
a
s
e
d
w
a
t
e
r
q
u
a
l
i
t
y
mo
n
i
t
o
r
i
n
g
sy
st
e
m
u
si
n
g
L
o
R
a
t
e
c
h
n
o
l
o
g
y
,
”
T
E
M
J
o
u
r
n
a
l
,
p
p
.
2
9
–
3
5
,
F
e
b
.
2
0
2
3
,
d
o
i
:
1
0
.
1
8
4
2
1
/
TE
M
1
2
1
-
0
4
.
[
5
]
A
.
N
i
k
n
a
m,
H
.
K
.
Za
r
e
,
H
.
H
o
ss
e
i
n
i
n
a
sa
b
,
A
.
M
o
s
t
a
f
a
e
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[
19
]
J.
G
u
b
b
i
,
R
.
B
u
y
y
a
,
S
.
M
a
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si
c
,
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n
d
M
.
P
a
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sw
a
mi
,
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o
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T
h
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s
(
I
o
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:
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V
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o
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El
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men
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2
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3
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0
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.
0
1
0
.
[
20
]
A
.
B
o
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a
,
W
.
d
e
D
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o
,
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.
P
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o
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A
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p
é
,
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r
a
t
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o
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.
[
2
1
]
S
.
A
.
P
a
l
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r
m
o
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t
a
l
.
,
“
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c
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M
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t
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O
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0
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3
3
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/
s2
2
1
6
6
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2
5
.
[
22
]
M
.
H
e
r
r
e
r
a
,
L.
T
o
r
g
o
,
J.
I
z
q
u
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o
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a
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d
R
.
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r
e
z
-
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a
r
c
í
a
,
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r
e
d
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c
t
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M
o
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f
o
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F
o
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c
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n
g
H
o
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l
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b
a
n
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m
a
n
d
,
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o
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rn
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y
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v
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2
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0
4
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5
.
[
23
]
X
.
W
a
n
g
,
Y
.
Li
,
a
n
d
Y
.
W
a
n
g
,
“
I
n
t
e
r
v
a
l
f
o
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c
a
s
t
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g
f
o
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d
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ma
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p
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d
K
D
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d
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st
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b
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t
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a
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d
LST
M
n
e
u
r
a
l
n
e
t
w
o
r
k
s
,
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p
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7
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.
[
24
]
S
.
K
.
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h
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ma
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d
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.
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u
mar
,
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w
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8.
[
25
]
A
.
Za
n
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l
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,
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.
B
u
i
,
A
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C
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s
f
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mart
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s,
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t
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RAP
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AUTH
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Ang
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b
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ter
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h
e
c
a
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b
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c
o
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tac
ted
a
t
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m
a
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:
a
tsa
tsa
@h
a
ra
re
c
it
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c
o
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z
w
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tsa
tsa
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m
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c
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m
.
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tsa
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st,
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g
in
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r
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ra
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stit
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m
b
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b
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d
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ra
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m
b
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rc
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it
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x
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wit
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sh
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p
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o
m
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larg
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h
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lex
AI
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c
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b
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c
o
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m
a
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u
tsa
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t.
a
c
.
z
w
.
Yo
la
n
d
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C
h
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a
y
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first
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ize
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lec
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r
in
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g
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t
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n
tre
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re
se
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rc
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l
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tel
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n
c
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b
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g
d
a
ta,
so
ftwa
re
e
n
g
i
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e
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rin
g
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ICT
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d
e
v
e
l
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m
e
n
t
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a
n
d
d
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g
it
a
l
tran
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ti
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S
h
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c
a
n
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c
o
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tac
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m
a
il
:
y
c
h
i
b
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y
a
@h
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t.
a
c
.
z
w
.
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