Geomatics Natural Hazards and Risk,
Journal Year:
2023,
Volume and Issue:
14(1)
Published: May 4, 2023
This
study
aims
to
examine
three
machine
learning
(ML)
techniques,
namely
random
forest
(RF),
LightGBM,
and
CatBoost
for
flooding
susceptibility
maps
(FSMs)
in
the
Vietnamese
Vu
Gia-Thu
Bon
(VGTB).
The
results
of
ML
are
compared
with
those
rainfall-runoff
model,
different
training
dataset
sizes
utilized
performance
assessment.
Ten
independent
factors
assessed.
An
inventory
map
approximately
850
sites
is
based
on
several
post-flood
surveys.
randomly
split
between
(70%)
testing
(30%).
AUC-ROC
97.9%,
99.5%,
99.5%
CatBoost,
RF,
respectively.
FSMs
developed
by
methods
show
good
agreement
terms
an
extension
flood
inundation
using
model.
models'
showed
10–13%
total
area
be
highly
susceptible
flooding,
consistent
RRI's
map.
that
downstream
areas
(both
urbanized
agricultural)
under
high
very
levels
susceptibility.
Additionally,
input
datasets
tested
determine
least
number
data
points
having
acceptable
reliability.
demonstrate
can
realistically
predict
FSMs,
regardless
samples.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Jan. 11, 2025
Flash
flood
susceptibility
mapping
is
essential
for
identifying
areas
prone
to
flooding
events
and
aiding
decision-makers
in
formulating
effective
prevention
measures.
This
study
aims
evaluate
the
flash
Yarlung
Tsangpo
River
Basin
(YTRB)
using
multiple
machine
learning
(ML)
models
facilitated
by
H2O
automated
ML
platform.
The
best-performing
model
was
used
generate
a
map,
its
interpretability
analyzed
Shapley
Additive
Explanations
(SHAP)
tree
interpretation
method.
results
revealed
that
top
four
models,
including
both
single
ensemble
demonstrated
high
accuracy
tests.
map
generated
eXtreme
Randomized
Trees
(XRT)
showed
8.92%,
12.95%,
15.42%,
31.34%,
31.37%
of
area
exhibited
very
high,
moderate,
low,
low
susceptibility,
respectively,
with
approximately
74.9%
historical
floods
occurring
classified
as
moderate
susceptibility.
SHAP
plot
identified
topographic
factors
primary
drivers
floods,
importance
analysis
ranking
most
influential
such
descending
order
DEM,
wetness
index,
position
normalized
difference
vegetation
average
multi-year
precipitation.
demonstrates
benefits
interpretable
learning,
which
can
provide
guidance
mitigation.
Ecological Indicators,
Journal Year:
2024,
Volume and Issue:
166, P. 112348 - 112348
Published: July 9, 2024
Cropland
resources
are
crucial
for
food
security
and
economic
development.
As
a
populous
nation
that
considers
cropland
valuable
strategic
resource,
China
has
faced
challenges
of
the
phenomenon
non-agriculturalization
(CLNA)
in
recent
years.
Studying
spatial
temporal
patterns
CLNA
different
regions
its
driving
factors
is
great
significance
formulating
improving
protection
policies.
Based
on
remotely
sensed
land
use
data,
evolution
characteristics
underlying
influencing
three
typical
(Jilin,
Henan,
Guangdong-Hong
Kong-Macao
Greater
Bay
Area)
from
2000
to
2020
were
revealed
by
using
Moran's
index,
Lorenz's
curve,
XGBoost-SHAP
model.
The
results
study
show
that:
(1).
There
was
certain
amount
all
during
past
20
years,
Area
(GHM)
most
serious,
with
area
exceeding
6
%
total
peak
period.
average
Gini
coefficients
Jilin
(JL),
Henan
(HN)
GHM
0.39,
0.45
0.77,
respectively,
imbalance
serious
GHM,
which
seriously
threatened
region.
(2).
In
areas,
socio-economic
contributed
68
86
84
CLNA,
significantly
higher
than
natural
factors,
dominant
CLNA.
(3)
Although
played
role
non-agriculturalization,
such
as
elevation
slope
should
not
be
overlooked.
These
only
directly
affect
usability
suitability
but
also
interact
jointly
shaping
trends
specific
regions,
constraints
may
hinder
conversion
non-agricultural
uses,
thus
playing
protective
role.
Conversely,
some
favorable
conditions
combine
strong
development
needs,
accelerating
transformation
uses.
This
interactive
effect
complex
process
influenced
multiple
factors.
Overall,
this
provides
an
in-depth
perspective
understanding
spatiotemporal
mechanisms
offers
scientific
basis
precisely
policies
promoting
sustainable
use.
