Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithms
Geomatics Natural Hazards and Risk,
Год журнала:
2025,
Номер
16(1)
Опубликована: Фев. 13, 2025
Язык: Английский
Unveiling global flood hotspots: Optimized machine learning techniques for enhanced flood susceptibility modeling
Journal of Hydrology Regional Studies,
Год журнала:
2025,
Номер
58, С. 102285 - 102285
Опубликована: Март 4, 2025
Язык: Английский
SAR-driven flood inventory and multi-factor ensemble susceptibility modelling using machine learning frameworks
Geomatics Natural Hazards and Risk,
Год журнала:
2024,
Номер
15(1)
Опубликована: Окт. 16, 2024
Climate
change
has
substantially
increased
both
the
occurrence
and
intensity
of
flood
events,
particularly
in
Indian
subcontinent,
exacerbating
threats
to
human
populations
economic
infrastructure.
The
present
research
employed
novel
ML
models—LR,
SVM,
RF,
XGBoost,
DNN,
Stacking
Ensemble—developed
Python
environment
leveraged
18
flood-influencing
factors
delineate
flood-prone
areas
with
precision.
A
comprehensive
inventory,
obtained
from
Sentinel-1
Synthetic
Aperture
Radar
(SAR)
data
using
Google
Earth
Engine
(GEE)
platform,
provided
empirical
for
entire
model
training
validation.
Model
performance
was
assessed
precision,
recall,
F1-score,
accuracy,
ROC-AUC
metrics.
results
highlighted
Ensemble's
superior
predictive
ability
(0.965),
followed
closely
by,
XGBoost
(0.934),
DNN
(0.929),
RF
(0.925),
LR
(0.921),
SVM
(0.920)
respectively,
establishing
feasibility
applications
disaster
management.
maps
depicting
susceptibility
flooding
generated
by
current
provide
actionable
insights
decision-makers,
city
planners,
authorities
responsible
management,
guiding
infrastructural
community
resilience
enhancements
against
risks.
Язык: Английский
Flood mapping, damage assessment, and susceptibility zonation in northeastern Bangladesh in 2022 using geospatial datasets
Progress in Disaster Science,
Год журнала:
2024,
Номер
unknown, С. 100402 - 100402
Опубликована: Дек. 1, 2024
Язык: Английский
Assessment of Flood Disaster Risk in the Lancang–Mekong Region
Water,
Год журнала:
2024,
Номер
16(21), С. 3112 - 3112
Опубликована: Окт. 30, 2024
The
Lancang–Mekong
Region
encompasses
six
countries,
covering
an
area
exceeding
five
million
square
kilometers
and
containing
a
population
of
more
than
400
million.
Floods
in
this
region
may
cause
extremely
serious
losses
lives
property.
However,
due
to
the
severe
shortage
flood
disaster
data,
loss
data
meteorological
monitoring
assessment
risks
remains
highly
formidable.
In
view
this,
we
systematically
integrated
from
EM-DAT
(the
Emergency
Events
Database),
Desinventar
(a
information
management
system),
Reliefweb
humanitarian
service
provided
by
United
Nations
Office
for
Coordination
Humanitarian
Affairs),
ADRC
Asian
Disaster
Reduction
Center),
coupled
with
GLDAS
(Global
Land
Data
Assimilation
System)
precipitation
economic
World
Bank,
comprehensively
considered
vulnerability,
exposure,
criteria
assess
Region.
research
findings
are
as
follows:
(1)
From
1965
2017,
total
370
floods
occurred
Region,
among
which
proportion
Vietnam
Thailand
combined
was
high
43.7%.
contrast,
number
Qinghai
Tibet
China
relatively
small,
only
1.89%.
(2)
When
mild
disasters
occur,
southern
part
Myanmar,
western
Thailand,
northeastern
faced
large
threats;
when
moderate
central
eastern
Cambodia,
comparatively
high-loss
areas
mainly
concentrated
Vietnam.
(3)
Considering
hazards
comprehensively,
high-risk
distributed
central–southern
Vietnam,
bordering
Cambodia
Vietnam;
medium-risk
Sichuan
China;
speaking,
other
have
lower
risk
level.
This
can
provide
references
regions
scarce
technical
support
prevention
control
well
Язык: Английский