Geomatics Natural Hazards and Risk,
Год журнала:
2024,
Номер
15(1)
Опубликована: Авг. 31, 2024
A
comprehensive
Flood
Resilient
Scenario
Model
'FReSMo'
employs
a
data-driven,
evidence-based
approach
for
assessing
climate-induced
flood
risk
and
validating
the
efficacy
of
mangroves
(as
context-specific
adaptation
measure)
in
reducing
residential
building
damage.
Based
on
an
improvised
Source-Pathway-Receptor-Consequence-Evidence
concept,
FReSMo
three-step
analysis.
First,
hazard
mapping
estimates
coastal
extents
various
return
period
under
different
Shared
Socioeconomic
Pathways.
Second,
model
maps
exposure
buildings
to
these
by
projecting
built-up
area
2050
using
FUTURES
model,
based
physiographic,
socio-demographic,
economic
parameters.
Finally,
data-driven
probabilistic
damage
is
applied
estimate
100-year
event
(SSP-2.6).
The
pre-and
post-adaptation
demonstrates
efficacity
NBS
(mangroves)
risk.
100
m
mangrove
patch
Sagar
coastline
reduced
cost
70%
48-hr
75%
24-hr
flood.
Considering
plantation
6.2
km2,
total
benefit,
despite
persistent
losses,
was
222%
investment.
transcends
conventional
assessment
frameworks
offering
evaluating
cost-effectiveness
investments
developing
countries,
making
it
invaluable
tool
face
climate
change.
Earth Systems and Environment,
Год журнала:
2023,
Номер
7(4), С. 733 - 760
Опубликована: Дек. 1, 2023
Abstract
Floods
represent
a
significant
threat
to
human
life,
property,
and
agriculture,
especially
in
low-lying
floodplains.
This
study
assesses
flood
susceptibility
the
Brahmaputra
River
basin,
which
spans
China,
India,
Bhutan,
Bangladesh—an
area
notorious
for
frequent
flooding
due
saturation
of
river
water
intake
capacity.
We
developed
evaluated
several
innovative
models
predicting
by
employing
Multi-Criteria
Decision
Making
(MCDM)
Machine
Learning
(ML)
techniques.
The
showed
robust
performance,
evidenced
Area
Under
Receiver
Operating
Characteristic
Curve
(AUC-ROC)
scores
exceeding
70%
Mean
Absolute
Error
(MAE),
Squared
(MSE),
Root
(RMSE)
below
30%.
Our
findings
indicate
that
approximately
one-third
studied
region
is
categorized
as
moderately
highly
flood-prone,
while
over
40%
classified
low
very
flood-risk
areas.
Specific
regions
with
high
include
Dhemaji,
Dibrugarh,
Lakhimpur,
Majuli,
Darrang,
Nalbari,
Barpeta,
Bongaigaon,
Dhubri
districts
Assam;
Coochbihar
Jalpaiguri
West
Bengal;
Kurigram,
Gaibandha,
Bogra,
Sirajganj,
Pabna,
Jamalpur,
Manikganj
Bangladesh.
Owing
their
strong
performance
suitability
training
datasets,
we
recommend
application
MCDM
techniques
ML
algorithms
geographically
similar
holds
implications
policymakers,
regional
administrators,
environmentalists,
engineers
informing
management
prevention
strategies,
serving
climate
change
adaptive
response
within
basin.
Water,
Год журнала:
2024,
Номер
16(8), С. 1141 - 1141
Опубликована: Апрель 17, 2024
Mapping
spatial
data
is
essential
for
the
monitoring
of
flooded
areas,
prognosis
hazards
and
prevention
flood
risks.
The
Ganges
River
Delta,
Bangladesh,
world’s
largest
river
delta
prone
to
floods
that
impact
social–natural
systems
through
losses
lives
damage
infrastructure
landscapes.
Millions
people
living
in
this
region
are
vulnerable
repetitive
due
exposure,
high
susceptibility
low
resilience.
Cumulative
effects
monsoon
climate,
rainfall,
tropical
cyclones
hydrogeologic
setting
Delta
increase
probability
floods.
While
engineering
methods
mitigation
include
practical
solutions
(technical
construction
dams,
bridges
hydraulic
drains),
regulation
traffic
land
planning
support
systems,
geoinformation
rely
on
modelling
remote
sensing
(RS)
evaluate
dynamics
hazards.
Geoinformation
indispensable
mapping
catchments
areas
visualization
affected
regions
real-time
monitoring,
addition
implementing
developing
emergency
plans
vulnerability
assessment
warning
supported
by
RS
data.
In
regard,
study
used
monitor
southern
segment
Delta.
Multispectral
Landsat
8-9
OLI/TIRS
satellite
images
were
evaluated
(March)
post-flood
(November)
periods
analysis
extent
landscape
changes.
Deep
Learning
(DL)
algorithms
GRASS
GIS
modules
qualitative
quantitative
as
advanced
image
processing.
results
constitute
a
series
maps
based
classified
Geomatics Natural Hazards and Risk,
Год журнала:
2024,
Номер
15(1)
Опубликована: Июль 3, 2024
Tropical
cyclones,
including
surge
inundation,
are
a
common
event
in
the
coastal
regions
of
Bangladesh.
The
washes
out
area
within
very
short
period
and
remains
flooded
condition
for
several
days.
Spatial
analysis
to
understand
susceptibility
level
can
assist
cyclone
management
system.
Surge
could
be
one
most
essential
parts
disaster
risk
reduction
through
which
vulnerability
minimized.
A
Geographic
Information
Systems-based
analytical
hierarchy
process
(AHP)
multi-criteria
bivariate
frequency
ratio
(FR)
techniques
were
conducted
cyclone-prone
on
Bangladesh
coast.
total
10
criteria
considered
influential
flooding,
i.e.
Topographic
Wetness
Index,
elevation,
wind
velocity,
slope,
distance
from
sea
rivers,
drainage
density,
Land
Use
Cover,
Normalized
Difference
Vegetation
precipitation,
soil
types.
final
maps
categorized
into
five
classes,
low,
moderate,
high,
high.
Conferring
these
policymakers
make
decisions
future
land
use
activities.
According
this
research,
AHP
showed
better
precision
(Receiver
Operating
Characteristic)
than
FR
prediction
IOP Conference Series Earth and Environmental Science,
Год журнала:
2024,
Номер
1300(1), С. 012012 - 012012
Опубликована: Фев. 1, 2024
Abstract
Finding
vulnerability
to
flooding
locations
is
a
crucial
part
of
sensible
urban
development
and
effective
natural
disaster
management.
Globally,
there
has
been
noticeable
rise
in
the
frequency
floods
recent
years,
which
affects
human
habitation
several
economic
sectors.
This
calls
for
employment
various
prevention
measures,
wherein
assessment
crucial.
The
main
objective
present
study
introduce
best
procedure
identification
flood
risk
detection
using
geographical
information
systems
techniques
decision-making,
based
on
comparative
evaluation
scenarios.
In
this
context,
current
will
develop
Topographic
Wetness
Index
(TWI)
tool
these
risks
can
deal
with
stream
orders,
calculate
length
valley,
then
show
outputs
by
thematic
maps.
developed
adaptive
applied
identify
Flood
Risk
Vulnerability
Erbil
city
some
surrounding
areas.
results
paper
indicated
existence
different
levels
TWI,
were
classified
into
five
classes.
an
advantage
over
other
traditional
methods
since
it
takes
account
mainly
statistics
data
that
are
linked
TWI
be
easily
customized
detecting
Vulnerability.
