Geoscience Frontiers,
Journal Year:
2021,
Volume and Issue:
12(6), P. 101230 - 101230
Published: May 27, 2021
The
geomorphic
studies
are
extremely
dependent
on
the
quality
and
spatial
resolution
of
digital
elevation
model
(DEM)
data.
unique
terrain
characteristics
a
particular
landscape
derived
from
DEM,
which
responsible
for
initiation
development
ephemeral
gullies.
As
topographic
features
an
area
significantly
influences
erosive
power
water
flow,
it
is
important
task
extraction
DEM
to
properly
research
gully
erosion.
Alongside,
topography
highly
correlated
with
other
geo-environmental
factors
i.e.
geology,
climate,
soil
types,
vegetation
density
floristic
composition,
runoff
generation,
ultimately
occurrences.
Therefore,
morphometric
attributes
data
used
in
prediction
erosion
susceptibility
(GES)
mapping.
In
this
study,
remote
sensing-Geographic
information
system
(GIS)
techniques
coupled
machine
learning
(ML)
methods
has
been
GES
mapping
parts
Semnan
province,
Iran.
Current
focuses
comparison
predicted
result
by
using
three
types
Advanced
Land
Observation
satellite
(ALOS),
ALOS
World
3D-30
m
(AW3D30)
Space
borne
Thermal
Emission
Reflection
Radiometer
(ASTER)
different
resolutions.
For
further
progress
our
work,
here
we
have
thirteen
suitable
conditioning
(GECFs)
based
multi-collinearity
analysis.
ML
conditional
inference
forests
(Cforest),
Cubist
Elastic
net
chosen
modelling
accordingly.
Variable's
importance
GECFs
was
measured
through
sensitivity
analysis
show
that
most
factor
occurrences
gullies
aforementioned
(Cforest
=
21.4,
19.65
17.08),
followed
lithology
slope.
Validation
model's
performed
under
curve
(AUC)
statistical
indices.
validation
AUC
shown
Cforest
appropriate
predicting
assessment
DEMs
(AUC
value
0.994,
AW3D30
0.989
ASTER
0.982)
elastic
cubist
model.
output
maps
will
be
decision-makers
sustainable
degraded
land
study
area.
Geological Journal,
Journal Year:
2023,
Volume and Issue:
58(6), P. 2372 - 2387
Published: Feb. 7, 2023
Landslide
susceptibility
analysis
can
provide
theoretical
support
for
landslide
risk
management.
However,
some
analyses
are
not
sufficiently
interpretable.
Moreover,
the
accuracy
of
many
research
methods
needs
to
be
improved.
Therefore,
this
study
supplement
these
deficiencies.
This
aims
evaluation
effects
random
forest
(RF)
and
extreme
gradient
boosting
(XGBoost)
classifier
models
on
susceptibility,
compare
their
applicability
in
Fengjie
County,
Chongqing,
a
typical
landslide‐prone
area
southwest
China.
Firstly,
1624
landslides
information
from
1980
2020
were
obtained
through
field
investigation,
geospatial
database
16
conditional
factors
had
been
constructed.
Secondly,
non‐landslide
points
selected
form
complete
data
set
RF
XGBoost
established.
Finally,
under
ROC
curve
(AUC)
value,
accuracy,
F
‐score
used
two
models.
The
results
show
that
even
though
both
classifiers
have
highly
accurate
model
performs
better.
In
comparison,
has
higher
AUC
value
0.866,
its
approximately
2%
than
XGBoost.
land
use,
elevation,
lithology
County
contribute
occurrence
landslides.
is
due
human
engineering
activities
(such
as
reclamation,
housing
construction)
resulting
low
slope
stability
widely
distributed
sandstone,
siltstone,
mudstone
layers
owing
permeability
planes
weakness.
Heliyon,
Journal Year:
2023,
Volume and Issue:
9(5), P. e16186 - e16186
Published: May 1, 2023
Predicting
landslides
is
becoming
a
crucial
global
challenge
for
sustainable
development
in
mountainous
areas.
This
research
compares
the
landslide
susceptibility
maps
(LSMs)
prepared
from
five
GIS-based
data-driven
bivariate
statistical
models,
namely,
(a)
Frequency
Ratio
(FR),
(b)
Index
of
Entropy
(IOE),
(c)
Statistical
(SI),
(d)
Modified
Information
Value
Model
(MIV)
and
(e)
Evidential
Belief
Function
(EBF).
These
models
were
tested
high
landslides-prone
humid
sub-tropical
type
Upper
Tista
basin
Darjeeling-Sikkim
Himalaya
by
integrating
GIS
remote
sensing.
The
inventory
map
consisting
477
locations
was
prepared,
about
70%
all
data
utilized
training
model,
30%
used
to
validate
it
after
training.
A
total
fourteen
triggering
parameters
(elevation,
slope,
aspect,
curvature,
roughness,
stream
power
index,
TWI,
distance
stream,
road,
NDVI,
LULC,
rainfall,
modified
fournier
lithology)
taken
into
consideration
preparing
LSMs.
multicollinearity
statistics
revealed
no
collinearity
problem
among
causative
factors
this
study.
Based
on
FR,
MIV,
IOE,
SI,
EBF
approaches,
12.00%,
21.46%,
28.53%,
31.42%,
14.17%
areas,
respectively,
identified
very
landslide-prone
zones.
also
that
IOE
model
has
highest
accuracy
95.80%,
followed
SI
(92.60%),
MIV
(92.20%),
FR
(91.50%),
(89.90%)
models.
Consistent
with
actual
distribution
landslides,
high,
medium
hazardous
zones
stretch
along
River
major
roads.
suggested
have
enough
usage
mitigation
long-term
land
use
planning
study
area.
Decision-makers
local
planners
may
utilise
study's
findings.
techniques
determining
can
be
employed
other
Himalayan
regions
manage
evaluate
hazards.
Journal of Hydrology Regional Studies,
Journal Year:
2021,
Volume and Issue:
36, P. 100848 - 100848
Published: June 26, 2021
The
present
study
has
been
carried
out
in
the
Tabriz
River
basin
(5397
km2)
north-western
Iran.
Elevations
vary
from
1274
to
3678
m
above
sea
level,
and
slope
angles
range
0
150.9
%.
average
annual
minimum
maximum
temperatures
are
2
°C
12
°C,
respectively.
rainfall
ranges
243
641
mm,
northern
southern
parts
of
receive
highest
amounts.
In
this
study,
we
mapped
groundwater
potential
(GWP)
with
a
new
hybrid
model
combining
random
subspace
(RS)
multilayer
perception
(MLP),
naïve
Bayes
tree
(NBTree),
classification
regression
(CART)
algorithms.
A
total
205
spring
locations
were
collected
by
integrating
field
surveys
data
Iran
Water
Resources
Management,
divided
into
70:30
for
training
validation.
Fourteen
conditioning
factors
(GWCFs)
used
as
independent
inputs.
Statistics
such
receiver
operating
characteristic
(ROC)
five
others
evaluate
performance
models.
results
show
that
all
models
performed
well
GWP
mapping
(AUC
>
0.8).
MLP-RS
achieved
high
validation
scores
=
0.935).
relative
importance
GWCFs
was
revealed
slope,
elevation,
TRI
HAND
most
important
predictors
presence.
This
demonstrates
ensemble
can
support
sustainable
management
resources.
