Enhancing resilience in isolated island communities: a disaster adaptation framework using importance-performance analysis
Yufang Lin,
No information about this author
Bih-Chuan Lin,
No information about this author
Chun-Hung Lee
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et al.
Natural Hazards,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 29, 2025
Language: Английский
Advancing flood risk assessment: Multitemporal SAR-based flood inventory generation using transfer learning and hybrid fuzzy-AHP-machine learning for flood susceptibility mapping in the Mahananda River Basin
Journal of Environmental Management,
Journal Year:
2025,
Volume and Issue:
380, P. 124972 - 124972
Published: March 23, 2025
The
Mahananda
River
basin,
located
in
Eastern
India,
faces
escalating
flood
risks
due
to
its
complex
hydrology
and
geomorphology,
threatening
socioeconomic
environmental
stability.
This
study
presents
a
novel
approach
susceptibility
(FS)
mapping
updates
the
region's
inventory.
Multitemporal
Sentinel-1
(S1)
SAR
images
(2020-2022)
were
processed
using
U-Net
transfer
learning
model
generate
water
body
frequency
map,
which
was
integrated
with
Global
Flood
Dataset
(2000-2018)
refined
through
grid-based
classification
create
an
updated
Eleven
geospatial
layers,
including
elevation,
slope,
soil
moisture,
precipitation,
type,
NDVI,
Land
Use
Cover
(LULC),
wind
speed,
drainage
density,
runoff,
used
as
conditioning
factors
(FCFs)
develop
hybrid
FS
approach.
integrates
Fuzzy
Analytic
Hierarchy
Process
(FuzzyAHP)
six
machine
(ML)
algorithms
models
FuzzyAHP-RF,
FuzzyAHP-XGB,
FuzzyAHP-GBM,
FuzzyAHP-avNNet,
FuzzyAHP-AdaBoost,
FuzzyAHP-PLS.
Future
trends
(1990-2030)
projected
CMIP6
data
under
SSP2-4.5
SSP5-8.5
scenarios
MIROC6
EC-Earth3
ensembles.
SHAP
algorithm
identified
LULC,
type
most
influential
FCFs,
contributing
over
60
%
susceptibility.
Results
show
that
31.10
of
basin
is
highly
susceptible
flooding,
western
regions
at
greatest
risk
low
elevation
high
density.
projections
indicate
30.69
area
will
remain
vulnerable,
slight
increase
SSP5-8.5.
Among
models,
FuzzyAHP-XGB
achieved
highest
accuracy
(AUC
=
0.970),
outperforming
FuzzyAHP-GBM
0.968)
FuzzyAHP-RF
0.965).
experimental
results
showed
proposed
can
provide
spatially
well-distributed
inventory
derived
from
freely
available
remote
sensing
(RS)
datasets
robust
framework
for
long-term
assessment
ML
techniques.
These
findings
offer
critical
insights
improving
management
mitigation
strategies
basin.
Language: Английский
Assessment and Monitoring of Flood Susceptibility Zones Using Analytical Hierarchy Process (AHP) Model and Geospatial Techniques in the Lakhimpur Block, Lakhimpur District, Assam, India
Environmental science and engineering,
Journal Year:
2025,
Volume and Issue:
unknown, P. 179 - 208
Published: Jan. 1, 2025
Language: Английский
Optimizing water management and climate-resilient agriculture in rice-fallow regions of the Dwarakeswar river basin using ML models
Chiranjit Singha,
No information about this author
Satiprasad Sahoo,
No information about this author
Ajit Govind
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et al.
Deleted Journal,
Journal Year:
2025,
Volume and Issue:
7(4)
Published: April 11, 2025
Language: Английский
Future flood susceptibility mapping under climate and land use change
Hamidreza Khodaei,
No information about this author
Farzin Nasiri Saleh,
No information about this author
Afsaneh Nobakht Dalir
No information about this author
et al.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: April 11, 2025
Language: Английский
Assessing the impact of climate change on flood patterns in downstream Nigeria using machine learning and geospatial techniques (2018-2024)
Desmond Rowland Eteh,
No information about this author
Bunakiye R. Japheth,
No information about this author
Charles Ugochukwu Akajiaku
No information about this author
et al.
Research Square (Research Square),
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 18, 2025
Abstract
Climate
change
has
increased
flood
risks
in
downstream
Nigeria,
driven
by
altered
hydrology,
dam
operations,
and
land-use
changes
threatening
infrastructure,
livelihoods,
ecosystem
stability
with
growing
frequency
severity.
This
study
analyzes
patterns,
identifies
key
environmental
drivers,
predicts
flood-prone
areas
through
an
integrated
machine
learning
geospatial
analysis
approach.
Data
sources
included
Synthetic
Aperture
Radar
(SAR)
imagery
from
Sentinel-1,
rainfall
measurements,
Shuttle
Topography
Mission
(SRTM)
elevation
data,
surface
water
level
records.
Machine
models
Random
Forest
(RF),
Support
Vector
(SVM),
Artificial
Neural
Network
(ANN)
were
applied
using
tools
such
as
Google
Earth
Engine
ArcGIS
10.5
to
assess
dynamics
2018
2024.
Downstream
regions
(elevation:
78–235.1
m)
exhibited
greater
susceptibility
than
upstream
(up
1399.43
m).
Flood
extents
rose
10.9%
August
(from
2441.91
km²
2707.75
2024)
39.8%
October
3083.44
4311.55
km²).
The
RF
model
achieved
the
highest
accuracy
(92%),
outperforming
SVM
(88%)
ANN
(85%).
Inundated
20–35%
of
zones.
Rainfall
intensity
15–20%,
annual
totals
exceeding
4311
mm
some
areas.
cover
declined
further
exacerbating
risks.
findings
demonstrate
that
climate
change,
alteration,
operations
are
major
contributors
flooding.
Mitigation
strategies
include
10–15%
reforestation,
embankment
construction,
learning–driven
early
warning
systems,
which
can
reduce
damage
up
30%.
These
approaches
support
sustainable
risk
management
Nigeria.
Language: Английский
Novel MCDA methods for flood hazard mapping: a case study in Hamadan, Iran
Stochastic Environmental Research and Risk Assessment,
Journal Year:
2024,
Volume and Issue:
38(12), P. 4863 - 4881
Published: Nov. 13, 2024
Language: Английский
Climate-resilient strategies for sustainable groundwater management in Mahanadi River basin of Eastern India
Acta Geophysica,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 20, 2024
Language: Английский
Assessment of Urban Flood Susceptibility and Inundation through Bivariate Statistics with Synthetic Aperture Radar: Insights for Spatial Planning in Pekanbaru City, Indonesia
Research Square (Research Square),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Sept. 25, 2024
Abstract
Flooding
has
become
one
of
the
most
dangerous
hydrometeorological
disasters,
affecting
sustainability
cities
in
future.
This
study
aims
to
assess
flood
susceptibility
using
a
frequency
ratio
approach
and
evaluate
spatial
planning
Pekanbaru
City,
Indonesia.
Flood
locations
were
derived
from
synthetic
aperture
radar
data
prepare
actual
data.
In
this
area,
identification
physical
environmental
parameters
was
conducted
various
datasets
such
as
slope,
landform,
curvature,
topographic
wetness
index,
distance
rivers,
rainfall,
soil
texture,
depth.
Furthermore,
weighted
assessment
all
thematic
layers
calculated
based
on
events
observation
area.
The
overall
related
location
divided,
with
70%
for
model
development
30%
validation.
results
showed
that
affected
18
km²,
an
accuracy
84.21%.
categorized
into
four
levels
very
high
(11.36%),
(58.04%),
medium
(24.78%),
low
(5.81%).
An
accurate
potential
susceptibility,
measured
by
operational
characteristic
curve
(AUC),
prediction
rate
76.30%
success
78.45%.
However,
considering
implications
patterns,
affects
cultivated
areas
covering
381.16
which
are
spread
almost
throughout
urban
High
indirectly
cause
disaster
losses
impact
community
activities.
misalignment
between
distribution
needs
be
addressed
anticipate
other
hazards.
Language: Английский
Investigation of precipitation trends in Lower Mekong Delta River Basin of Vietnam by innovative trend analysis methods
Theoretical and Applied Climatology,
Journal Year:
2024,
Volume and Issue:
155(12), P. 10033 - 10050
Published: Oct. 31, 2024
Language: Английский