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tec
h
n
iq
u
es a
r
e
u
s
i
n
g
w
id
el
y
i
n
p
r
ed
ictio
n
p
r
o
c
ess
.
2.
L
I
T
E
R
AT
U
RE
SU
RVE
Y
K.
S.
R
ed
d
y
et
al
[
4
]
ev
alu
a
t
ed
L
A
R
S
-
W
G
m
o
d
el
f
o
r
s
o
u
th
er
n
T
elan
g
a
n
a
an
d
An
d
h
r
a
P
r
ad
esh
r
eg
io
n
,
u
s
ed
t
h
ir
t
y
y
ea
r
cl
i
m
ate
s
tati
s
tics
f
r
o
m
1
9
8
0
to
2
0
1
0
to
p
r
o
d
u
ce
th
e
e
n
d
u
r
i
n
g
cli
m
ate
s
er
ies
f
o
r
2011
-
2
0
6
0
.
T
h
e
v
er
s
io
n
f
o
r
ec
asted
th
e
r
is
e
in
s
ta
n
d
ar
d
y
ea
r
l
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ain
f
all
i
n
2
0
3
0
is
5
.
1
6
%
an
d
in
2
0
6
0
is
9
.
5
%
f
o
r
Yac
h
ar
a
m
r
elate
d
to
Ha
y
a
th
n
ag
ar
.
I
t
w
as
r
a
n
k
ed
as
b
e
s
t
m
o
d
el
i
n
ter
m
s
o
f
e
f
f
ec
ti
v
e
n
es
s
in
a
ll
s
elec
ted
R
an
g
ar
ed
d
y
m
a
n
d
als
o
f
T
elan
g
a
n
a
a
n
d
i
s
ap
p
lied
to
all
o
th
er
m
a
n
d
als
b
ec
au
s
e
th
e
c
li
m
atic
co
n
d
itio
n
s
ar
e
s
i
m
ilar
in
t
h
o
s
e
r
eg
io
n
s
.
I
s
h
ap
p
a
Mu
n
i
y
ap
p
a
R
ath
o
d
et
al
[
5
]
id
en
tif
ied
th
at
th
e
r
ain
f
all
is
th
e
v
ita
l
asp
ec
t
w
h
ic
h
d
ec
id
es
th
e
cr
o
p
an
d
y
ield
m
o
d
el
o
f
a
r
eg
io
n
.
T
h
e
s
u
cc
e
s
s
a
n
d
f
ailu
r
e
o
f
t
h
e
cr
o
p
d
ep
en
d
s
u
p
o
n
th
e
cl
i
m
a
tic
cir
cu
m
s
ta
n
ce
s
.
T
h
e
y
s
t
u
d
ied
th
e
p
r
ec
ip
itatio
n
tr
aits
li
k
e
s
p
atial
allo
ca
tio
n
,
s
ea
s
o
n
al
v
ar
iab
ilit
y
o
f
t
h
e
C
o
i
m
b
ato
r
e
d
is
tr
ict.
T
h
e
r
esea
r
ch
is
b
u
ilt o
n
Fo
r
t
y
Nin
e
y
ea
r
s
o
f
r
ai
n
f
a
ll d
ata
f
o
r
th
ir
t
y
t
h
r
ee
r
ain
s
ca
le
p
lace
s
.
T
h
er
e
is
a
h
ea
v
y
p
r
ec
ip
itatio
n
in
n
o
r
t
h
,
s
o
u
t
h
p
ar
ts
an
d
les
s
p
r
ec
ip
itatio
n
in
ea
s
t p
ar
t o
f
th
e
d
is
tr
ict.
R
aj
in
ik
a
n
t
h
T
V
et
al
[
6
]
s
ta
t
ed
th
at
th
er
e
ar
e
a
q
u
ite
a
lo
t
o
f
cli
m
atic
co
n
d
itio
n
s
i
n
v
a
r
io
u
s
ti
m
e
p
er
io
d
s
th
at
ar
e
v
ar
ied
g
eo
lo
g
icall
y
.
I
t
h
a
s
s
u
b
s
tan
tial
p
r
ec
ip
itatio
n
in
C
h
ir
ap
u
n
j
i,
h
i
g
h
war
m
th
at
R
aj
asth
a
n
an
d
co
ld
en
v
ir
o
n
m
e
n
t
at
Hi
m
ala
y
as.
T
h
ese
e
x
tr
e
m
e
s
m
a
k
e
u
s
u
n
co
m
f
o
r
tab
le
an
d
p
r
ed
ictio
n
s
o
f
cl
i
m
a
t
e
r
eq
u
ir
es
s
y
s
te
m
atic
ap
p
r
o
ac
h
es
lik
e
m
ac
h
in
e
lear
n
i
n
g
p
r
o
ce
d
u
r
es,
K
-
m
ea
n
s
al
g
o
r
ith
m
,
J
4
8
class
if
icatio
n
m
et
h
o
d
s
f
o
r
ef
f
icie
n
t st
u
d
y
a
n
d
ex
tr
ap
o
latio
n
s
o
f
cli
m
atic
co
n
d
itio
n
s
.
Ku
s
r
e
B
.
C
.
et
al
[
7
]
an
al
y
ze
d
th
e
s
p
atio
te
m
p
o
r
al
d
is
p
ar
it
y
o
f
t
h
e
p
r
ec
ip
itatio
n
in
Nag
alan
d
.
T
h
e
s
tu
d
y
il
lu
s
tr
ates
th
a
t
th
er
e
is
a
h
u
g
e
d
is
s
i
m
ilar
it
y
i
n
th
e
p
r
ec
ip
itatio
n
w
ith
d
is
p
ar
it
y
f
r
o
m
8
5
9
m
m
to
2
1
2
3
m
m
.
Yea
r
l
y
r
ain
f
all
m
o
d
el
i
n
d
icate
s
t
h
e
n
o
r
th
er
n
p
ar
t h
a
s
h
i
g
h
r
ain
f
all
as r
elate
d
to
ea
s
t,
west o
f
Nag
a
lan
d
.
I
n
th
e
s
a
m
e
w
a
y
t
h
e
n
o
r
th
p
ar
t
r
ec
eiv
es
m
o
r
e
r
ai
n
f
a
ll
i
n
m
o
n
s
o
o
n
s
ea
s
o
n
a
n
d
les
s
r
ain
f
all
i
n
w
i
n
ter
s
ea
s
o
n
as
r
elate
d
to
ea
s
t a
n
d
w
es
t p
ar
t o
f
Nag
alan
d
.
Ma
r
c
G.
Gen
to
n
et
al
[
8
]
s
tated
th
at
t
h
e
u
s
e
o
f
v
i
g
o
r
o
u
s
g
eo
-
s
tat
is
tica
l
tech
n
iq
u
e
s
o
n
t
h
e
s
tatis
tic
s
o
f
r
ain
f
al
l d
i
m
e
n
s
io
n
s
f
o
r
S
w
i
tze
r
lan
d
.
T
h
ey
ar
e
d
e
-
tr
en
d
ed
t
h
r
o
u
g
h
n
o
n
p
ar
a
m
etr
ic
ap
p
r
o
x
i
m
atio
n
w
it
h
le
v
eli
n
g
f
ac
to
r
.
T
h
e
f
in
e
s
t
tr
e
n
d
i
s
ca
lc
u
lated
w
i
th
a
f
latte
n
i
n
g
f
ac
to
r
b
ased
o
n
cr
o
s
s
v
a
lid
atio
n
.
T
h
e
v
ar
io
g
r
a
m
is
th
e
n
ca
lcu
lated
b
y
a
v
ig
o
r
o
u
s
ev
a
lu
ato
r
.
T
h
e
p
a
r
am
etr
ic
v
ar
io
g
r
a
m
p
r
o
to
ty
p
e
is
co
m
p
r
eh
e
n
d
ed
b
y
co
n
s
id
er
in
g
v
ar
ian
ce
–
co
v
ar
ia
n
ce
co
m
p
o
s
itio
n
o
f
v
ar
io
g
r
a
m
ap
p
r
o
x
i
m
ate
s
.
Fas
ci
n
ati
n
g
o
u
tco
m
e
s
ar
e
y
ield
ed
b
y
co
m
p
ar
i
n
g
k
r
ig
in
g
w
it
h
in
i
ti
al
q
u
an
titi
e
s
.
A
ll
o
f
t
h
ese
e
s
ti
m
ates
ar
e
d
o
n
e
w
i
th
n
e
w
m
eth
o
d
s
i
n
„
S+
SP
A
T
I
AL
ST
A
T
S‟
s
o
f
t
w
ar
e.
C
.
Sar
ala
et
al
[
9
]
s
tated
th
at
r
ain
f
al
l
is
ir
r
eg
u
lar
i
n
I
n
d
ia.
I
t
p
r
esen
ts
r
ai
n
f
al
l
an
al
y
s
is
b
y
ta
k
i
n
g
g
eo
lo
g
ical
m
et
h
o
d
in
p
r
ep
ar
atio
n
o
f
m
ap
s
in
g
eo
g
r
ap
h
ic
al
s
y
s
te
m
s
an
d
ch
ar
ac
ter
izi
n
g
s
p
atial,
te
m
p
o
r
al
d
is
s
e
m
in
at
io
n
o
f
m
o
n
t
h
l
y
an
d
y
ea
r
l
y
r
ai
n
f
all
i
n
T
elan
g
a
n
a
w
i
th
th
e
h
elp
o
f
tr
en
d
e
x
p
lo
r
atio
n
.
T
h
e
in
itia
l
s
tu
d
y
i
s
b
u
ilt
o
n
th
e
i
n
f
o
r
m
ati
o
n
f
r
o
m
1
0
d
is
tr
icts
an
d
4
5
7
m
an
d
al
s
.
I
n
th
is
a
n
al
y
s
is
,
n
u
m
er
o
u
s
GI
S
r
e
m
o
te
s
en
s
o
r
p
r
ac
tices
w
er
e
u
s
ed
b
y
i
n
co
r
p
o
r
atin
g
v
ar
io
u
s
g
eo
r
ef
er
en
ce
d
ata
s
et
s
i
n
t
h
e
g
e
n
er
atio
n
o
f
m
ap
s
o
f
r
ain
f
al
l in
T
elan
g
a
n
a.
S.
Nag
i
n
i,
R
aj
in
i
k
an
th
T
.
V.
et
al
[
1
0
]
s
tated
in
th
eir
p
ap
er
titl
ed
“
E
f
f
ec
ti
v
e
An
al
y
s
is
o
f
L
a
n
d
Su
r
f
ac
e
W
ater
R
eso
u
r
ce
s
o
f
A
n
d
h
r
a
P
r
ad
esh
w
it
h
R
o
u
g
h
Se
t
b
ased
H
y
b
r
id
Data
Min
in
g
T
ec
h
n
iq
u
es
U
s
i
n
g
R
”,
t
h
at
Ag
r
icu
ltu
r
e
p
la
y
s
an
i
m
p
o
r
ta
n
t
r
o
le
in
ec
o
n
o
m
y
o
f
I
n
d
ia.
M
o
r
e
th
an
h
al
f
o
f
t
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e
p
o
p
u
latio
n
i
n
I
n
d
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d
e
p
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s
o
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Ag
r
ic
u
lt
u
r
e.
I
t
p
r
o
v
id
es
r
a
w
m
ater
ial
f
o
r
m
an
y
I
n
d
u
s
tr
ie
s
.
I
n
ea
r
l
y
d
a
y
s
,
m
o
r
e
th
a
n
h
al
f
o
f
t
h
e
lan
d
m
as
s
i
s
u
s
ed
f
o
r
Ag
r
icu
l
tu
r
e
an
d
o
v
er
th
e
y
ea
r
s
t
h
er
e
is
d
ec
li
n
e
in
ag
r
ic
u
lt
u
r
e
la
n
d
.
Var
io
u
s
f
ac
to
r
s
li
k
e
t
h
e
u
r
b
an
izatio
n
an
d
d
ev
elo
p
m
e
n
t
r
esu
lt
s
in
t
h
e
g
r
o
w
t
h
o
f
No
n
-
Ag
r
icu
ltu
r
e
la
n
d
y
ea
r
b
y
y
ea
r
.
Ag
r
icu
l
tu
r
e
i
s
th
e
lar
g
est
ab
s
tr
ac
to
r
an
d
p
r
i
m
e
c
o
n
s
u
m
er
o
f
g
r
o
u
n
d
w
ater
r
eso
u
r
ce
s
ac
r
o
s
s
t
h
e
g
lo
b
e
an
d
h
e
n
ce
s
t
u
d
ies
o
f
a
g
r
o
-
ec
o
n
o
m
ies
t
h
at
ar
e
g
r
o
u
n
d
w
a
ter
d
ep
en
d
en
t b
ec
a
m
e
w
id
el
y
p
o
p
u
lar
.
Ag
r
icu
l
tu
r
e
I
r
r
i
g
atio
n
,
Su
r
f
ac
e
w
a
ter
an
d
Gr
o
u
n
d
w
ater
r
eso
u
r
ce
s
ar
e
i
n
ter
li
n
k
ed
to
ea
c
h
o
t
h
er
.
W
ater
Usag
e
a
n
d
Fo
o
d
P
r
o
d
u
ctio
n
ar
e
d
ep
en
d
en
t
o
n
ea
ch
o
t
h
er
e
x
ten
s
i
v
el
y
.
W
ater
is
t
h
e
m
aj
o
r
p
ar
a
m
eter
t
h
at
co
n
tr
o
ls
th
e
cr
o
p
y
ield
.
I
n
m
an
y
co
u
n
tr
ies,
th
e
ag
r
icu
l
tu
r
e
y
iel
d
d
ep
en
d
s
o
n
th
e
r
ain
f
all.
Ma
n
y
ti
m
es,
t
h
e
r
ain
f
all
i
s
n
o
t
s
u
f
f
ic
ien
t
to
c
r
o
p
y
ield
s
.
I
t
m
ad
e
r
esear
ch
er
s
to
d
o
r
ig
o
r
o
u
s
a
n
al
y
s
i
s
o
n
w
ater
r
eso
u
r
ce
av
ailab
ilit
y
a
n
d
s
u
g
g
est
f
ar
m
er
s
f
o
r
its
e
f
f
ec
ti
v
e
u
tili
za
t
io
n
.
T
h
is
p
ap
er
aim
s
at
,
d
ev
elo
p
m
e
n
t
an
d
ap
p
lica
tio
n
o
f
n
e
w
H
y
b
r
id
Data
Min
i
n
g
(
HDM
)
T
ec
h
n
iq
u
es
f
o
r
ef
f
ec
ti
v
e
a
n
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a
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i
s
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also
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ad
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v
ar
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A
g
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ic
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s
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a
m
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Kh
ar
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ab
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u
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ar
ca
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e,
Ma
ize,
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a
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i,
W
h
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t,
B
ar
le
y
,
etc.
,
u
s
i
n
g
n
e
w
H
y
b
r
id
Data
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
7
,
No
.
1
,
Feb
r
u
ar
y
201
7
:
4
6
0
–
468
462
Min
i
n
g
(
HDM
)
tech
n
iq
u
es.
T
o
m
o
d
el
th
e
co
m
p
le
x
lo
g
ic,
Dec
is
io
n
T
ab
les
(
D
T
)
is
u
s
ed
.
T
h
e
r
esu
lts
w
er
e
p
r
o
v
ed
to
b
e
g
o
o
d
w
h
e
n
n
e
w
R
o
u
g
h
Set
B
ased
H
y
b
r
id
Da
ta
Min
in
g
(
R
SB
HDM
)
T
ec
h
n
iq
u
es
ar
e
ap
p
lied
o
v
er
th
e
r
ef
i
n
ed
d
ata
s
ets.
N.
R
aj
asek
h
ar
,
R
aj
in
iKa
n
t
h
T
.
V.
[
1
1
]
s
tated
th
at
W
ea
th
er
P
r
ed
ictio
n
is
th
e
ap
p
licatio
n
o
f
s
cie
n
ce
an
d
tech
n
o
lo
g
y
to
e
s
ti
m
ate
th
e
s
tate
o
f
t
h
e
at
m
o
s
p
h
er
e
at
t
h
e
p
ar
ticu
lar
s
p
atial
lo
ca
ti
o
n
.
D
u
e
to
t
h
e
av
ailab
ilit
y
o
f
h
u
g
e
d
ata
r
esea
r
ch
er
s
,
g
o
t
i
n
ter
est
to
an
a
l
y
z
e
an
d
f
o
r
ec
ast
t
h
e
w
ea
t
h
er
.
A
cc
u
r
ate
p
r
ed
ictio
n
h
elp
s
t
h
e
h
u
m
an
b
ei
n
g
e
x
is
t
en
ce
an
d
p
r
o
s
p
er
it
y
.
Fo
r
ec
ast
in
g
tec
h
n
iq
u
e
s
ar
e
h
elp
f
u
l
i
n
p
r
ed
ictin
g
n
at
u
r
al
d
is
aster
s
,
cr
o
p
an
d
j
u
n
g
le
g
r
o
w
t
h
,
n
a
u
tica
l
r
o
u
tin
g
,
air
cr
af
t
s
ch
e
m
i
n
g
a
n
d
ar
m
ed
f
u
n
ctio
n
s
.
T
h
e
Data
Min
i
n
g
tech
n
iq
u
es
ar
e
b
etter
t
h
an
t
h
e
o
b
tain
ab
le
m
et
h
o
d
o
lo
g
ies
o
r
co
n
v
en
tio
n
al
m
e
th
o
d
s
.
T
h
e
y
w
er
e
p
r
o
j
ec
ted
h
y
b
r
id
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
r
ep
lica
to
p
r
ed
ict,
an
aly
ze
t
h
e
cli
m
a
tic
d
at
a
an
d
to
d
is
co
v
er
th
e
p
r
o
to
ty
p
e
s
ex
is
t
in
it.
T
h
e
y
co
n
s
id
er
ed
Kr
is
h
n
a
d
is
tr
ic
t
cli
m
ate
d
ata
f
o
r
th
e
ca
s
e
s
tu
d
y
a
n
d
it
p
r
o
d
u
ce
d
h
i
g
h
q
u
alit
y
r
esu
lt
s
r
ath
er
th
a
n
m
ac
h
in
e
lea
r
n
in
g
m
eth
o
d
s
i
n
th
e
p
r
o
ce
s
s
o
f
p
r
ed
ictio
n
.
An
a
n
th
o
j
u
V
ij
ay
K
u
m
ar
,
R
aj
in
iKa
n
th
T
.
V.
[
1
2
]
s
tated
th
at
th
e
r
ai
n
f
a
ll
h
as
in
ten
s
e
co
n
s
e
q
u
en
ce
o
n
ag
r
icu
l
tu
r
e.
A
s
ta
n
d
ar
d
r
ain
f
all
is
cr
u
cial
f
o
r
v
e
g
etatio
n
.
E
x
ce
s
s
i
v
e
o
r
d
i
m
i
n
u
tiv
e
r
a
in
f
all
ca
n
d
a
m
ag
e
cu
lti
v
atio
n
.
Di
m
i
n
u
tiv
e
ca
n
ab
o
lis
h
cu
l
tiv
at
io
n
a
n
d
e
x
ce
s
s
i
v
e
ca
n
h
elp
to
g
r
o
w
d
a
n
g
er
o
u
s
f
u
n
g
u
s
.
C
u
lti
v
atio
n
in
I
n
d
ia
lar
g
e
l
y
d
ep
en
d
s
o
n
r
ain
f
all,
s
o
an
e
f
f
o
r
t
is
m
ad
e
t
o
f
o
r
ec
ast
th
e
s
t
i
m
u
l
u
s
o
f
r
ain
f
a
ll
o
n
h
ar
v
est
o
f
g
r
o
u
n
d
n
u
t.
Fo
r
th
is
t
h
e
d
ata
s
et
is
co
n
s
tr
u
cted
w
i
th
y
ea
r
l
y
c
ap
ac
ities
o
f
cr
o
p
an
d
r
ain
f
all
f
o
r
s
ix
t
y
t
w
o
y
ea
r
s
.
T
h
e
d
ata
w
as
g
at
h
er
ed
f
r
o
m
v
ar
io
u
s
Go
v
er
n
m
e
n
t
s
ec
t
o
r
s
.
T
h
e
in
v
est
ig
atio
n
ex
p
o
s
ed
th
at
th
e
cr
o
p
is
d
estru
cti
v
el
y
p
r
ej
u
d
iced
b
y
r
ai
n
f
a
ll
3.
P
RO
P
O
SE
D
AP
P
RO
ACH
I
n
th
e
P
r
o
p
o
s
ed
a
p
p
r
o
ac
h
in
iti
all
y
t
h
e
v
ar
io
u
s
y
ea
r
s
r
ai
n
f
al
l
d
ata
o
f
An
d
h
r
a
P
r
ad
esh
,
T
ela
n
g
a
n
a
w
a
s
tak
en
an
d
p
r
ep
r
o
ce
s
s
ed
f
o
r
cl
ea
n
in
g
,
r
e
m
o
v
al
o
f
r
ed
u
n
d
an
c
y
,
f
illi
n
g
th
e
m
i
s
s
i
n
g
v
alu
e
s
w
it
h
s
u
itab
le
m
ea
n
v
alu
e
s
a
n
d
m
o
ld
ed
in
to
r
eq
u
ir
ed
f
o
r
m
at.
T
h
e
n
ap
p
l
y
h
y
b
r
id
izatio
n
o
f
Data
Mi
n
in
g
(
H
DM
)
T
ec
h
n
iq
u
e
s
o
n
t
h
e
p
r
ep
r
o
ce
s
s
ed
R
ain
f
all
d
ataset.
T
h
e
r
esu
lts
t
h
u
s
o
b
tain
ed
w
er
e
an
al
y
ze
d
ef
f
ec
tiv
e
l
y
b
y
c
o
n
s
tr
u
ct
in
g
v
ar
io
u
s
GI
S
Ma
p
s
u
s
in
g
t
h
e
R
ai
n
f
all
d
ata
s
et
w
it
h
t
h
e
h
elp
o
f
R
s
o
f
t
w
ar
e
[
1
4
]
.
I
t
h
as
p
r
o
v
ed
th
at
t
h
er
e
is
a
s
u
b
s
ta
n
tial
p
r
o
g
r
ess
in
p
er
f
o
r
m
a
n
ce
.
4.
I
M
P
L
E
M
E
NT
AT
I
O
N
O
F
P
RO
P
O
SE
D
M
E
T
H
O
DO
L
O
G
Y
I
n
itiall
y
th
e
r
a
w
s
p
atial
d
a
ta
s
et
is
P
r
e
-
p
r
o
ce
s
s
ed
an
d
co
n
v
er
ted
in
to
th
e
r
eq
u
ir
ed
f
o
r
m
at
th
u
s
o
b
tain
ed
is
ca
lled
r
ef
in
ed
s
p
atial
d
ata
s
et,
s
u
itab
le
f
o
r
f
u
r
th
er
p
r
o
ce
s
s
in
g
.
I
n
f
o
-
Gai
n
A
ttrib
u
te
E
v
al
u
atio
n
p
r
o
ce
d
u
r
e
alo
n
g
w
it
h
R
a
n
k
er
A
l
g
o
r
ith
m
is
ap
p
lied
an
d
attr
i
b
u
tes
s
e
lectio
n
w
as
d
o
n
e.
T
h
i
s
co
n
ce
p
t
f
in
d
s
th
e
v
alu
e
o
f
an
attr
ib
u
te
b
y
m
ea
s
u
r
i
n
g
i
n
f
o
r
m
atio
n
g
ai
n
f
o
r
a
g
i
v
e
n
cla
s
s
.
T
h
e
o
p
ti
m
ized
s
p
atial
d
ata
s
et
i
s
d
iv
id
ed
in
to
T
r
ain
d
ata
s
et
a
n
d
T
est
d
ata
s
et.
I
t
i
s
t
h
en
s
u
b
j
ec
ted
to
Ma
ch
in
e
lear
n
in
g
A
lg
o
r
it
h
m
n
a
m
el
y
C
las
s
i
f
icatio
n
al
g
o
r
ith
m
o
f
Data
m
i
n
i
n
g
tec
h
n
iq
u
e
ca
lle
d
J
4
8
tr
ee
class
if
icatio
n
.
T
h
e
p
er
f
o
r
m
a
n
ce
i
s
ca
lcu
lated
an
d
t
h
e
r
esu
ltan
t
d
ec
is
io
n
T
r
ee
J
4
8
class
if
ier
w
it
h
r
ef
in
ed
d
ata
s
et
i
s
s
u
b
j
ec
ted
t
o
A
s
s
o
ciatio
n
R
u
l
e
Min
i
n
g
A
l
g
o
r
ith
m
n
a
m
el
y
A
p
r
io
r
i
A
l
g
o
r
ith
m
.
T
h
en
t
h
e
g
en
er
ated
Ass
o
ciatio
n
R
u
le
s
w
il
l
b
e
an
al
y
ze
d
f
o
r
t
h
e
p
atter
n
s
.
T
h
e
r
ef
in
ed
s
p
atial
d
ata
s
et
is
u
s
ed
to
co
n
s
tr
u
ct
cu
s
to
m
ized
m
ap
s
[
1
7
]
u
s
in
g
r
eq
u
ir
ed
R
s
o
f
t
w
ar
e
co
d
e.
T
h
e
v
is
u
al
a
n
al
y
tic
s
wer
e
u
s
ed
f
o
r
s
p
atial
a
n
al
y
s
i
s
o
f
th
e
r
ain
f
all
d
ata
s
et
s
o
f
A
n
d
h
r
a
P
r
ad
esh
a
n
d
T
elan
g
an
a
s
ta
tes.
5.
RE
SU
L
T
S AN
D
AN
AL
Y
SI
S
T
h
e
attr
ib
u
tes
o
f
t
h
e
R
ai
n
f
all
d
ata
s
et
ar
e
n
a
m
e
l
y
Stat
e,
Dis
t
r
ict,
L
atit
u
d
e,
L
o
n
g
i
tu
d
e,
Yea
r
,
J
an
u
ar
y
,
Ma
r
ch
,
A
p
r
il,
Ma
y
,
J
u
n
e,
J
u
l
y
,
Au
g
u
s
t,
Sep
te
m
b
er
,
Octo
b
er
,
No
v
e
m
b
er
,
Dec
em
b
er
an
d
A
n
n
u
al
T
o
tal.
T
h
e
I
n
f
o
-
Gai
n
o
f
t
h
e
attr
ib
u
te
s
w
a
s
ca
lcu
lated
a
n
d
it
w
as
f
o
u
n
d
th
at,
ex
ce
p
t
t
h
e
attr
ib
u
tes
n
a
m
el
y
State,
D
is
tr
ict,
L
o
n
g
it
u
d
e,
L
atit
u
d
e,
Ma
y
,
J
u
l
y
,
Au
g
u
s
t,
Octo
b
er
,
No
v
e
m
b
e
r
,
Dec
e
m
b
er
,
o
th
er
attr
ib
u
tes
h
as
ze
r
o
I
n
f
o
-
Gai
n
v
alu
e
s
.
Af
ter
t
h
at
t
h
e
C
las
s
i
f
ic
atio
n
A
l
g
o
r
ith
m
C
las
s
f
o
r
g
e
n
er
atin
g
a
p
r
u
n
ed
o
r
u
n
-
p
r
u
n
ed
C
4
.
5
d
ec
is
io
n
tr
ee
k
n
o
w
n
as
J
4
8
[
1
5
]
is
ap
p
lie
d
an
d
th
e
r
esu
lta
n
t
C
las
s
i
f
ie
r
D
ec
is
io
n
T
r
ee
r
ep
r
esen
ted
b
y
Fig
u
r
e
1
.
T
h
is
Dec
is
io
n
tr
ee
s
a
y
s
t
h
at
th
e
An
d
h
r
a
P
r
ad
esh
an
d
T
elan
g
a
n
a
lies
in
b
et
w
ee
n
L
o
n
g
i
tu
d
e
b
o
u
n
d
ar
ies
ar
e
7
7
.
6
0
1
,
8
3
.
8
9
7
w
h
er
e
as t
h
e
L
a
tit
u
d
e
b
o
u
n
d
ar
ies ar
e
1
9
.
6
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tern
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A
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1
1
,
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CM
-
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0
1
3
,
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KT
,
IT
QM,
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e
tc.,
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h
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s
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lso
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d
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ro
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ra
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d
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E
p
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s Co
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Re
v
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w
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m
m
it
tee
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e
m
b
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r
\
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it
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rial
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rd
m
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m
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r
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tern
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ti
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l
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r
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m
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A
E
GT
,
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,
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,
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A
EN
T
,
e
tc.,
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w
a
s
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n
a
u
th
o
r
f
o
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f
e
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e
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rti
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icia
l
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telli
g
e
n
c
e
e
tc.
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c
u
rre
n
t
re
se
a
rc
h
a
re
a
in
tere
sts
in
c
lu
d
e
Im
a
g
e
p
ro
c
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ss
in
g
,
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ta
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re
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o
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sin
g
&
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in
i
n
g
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p
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ti
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d
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ta
m
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e
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m
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e
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t
m
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g
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n
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b
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ti
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se
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tl
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id
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ts
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th
e
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se
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rc
h
a
re
a
s
li
k
e
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p
a
ti
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l
d
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ta
m
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g
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e
b
m
in
in
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im
a
g
e
P
ro
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ss
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n
d
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e
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t
m
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g
.
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h
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s
c
o
n
d
u
c
ted
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o
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tern
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l
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n
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n
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l
y
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A
CM
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,
a
n
d
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CM
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a
t
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RIE
T
,
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y
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ra
b
a
d
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s
Co
n
v
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n
e
r
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n
d
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lso
a
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ted
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s
s
e
ss
io
n
c
h
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ir
f
o
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m
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n
y
c
o
n
f
e
re
n
c
e
s
li
k
e
IC
A
C
T
-
0
8
,
ICRS
KT
-
2
0
1
4
e
tc.
He
is
p
re
se
n
t
ly
g
u
id
in
g
(su
p
e
rv
iso
r
&
c
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su
p
e
rv
iso
r
lev
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l)
m
a
n
y
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h
.
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sc
h
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lars
a
t
v
a
rio
u
s
u
n
iv
e
rsiti
e
s
n
a
m
e
l
y
J
NT
U
H,
JN
TUK,
JN
T
U
A
a
n
d
A
NU
.
He
w
a
s
c
a
ll
e
d
f
o
r
a
ro
u
n
d
5
5
A
ICT
E
sp
o
n
so
re
d
\
T
EQIP
w
o
rk
sh
o
p
s
a
s
re
so
u
rc
e
p
e
rso
n
.
He
is
L
if
e
M
e
m
b
e
r
in
IS
T
E,
CS
I
a
n
d
a
m
e
m
b
e
r
in
I
EEE
.
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