2021 International Conference on Emerging Smart Computing and Informatics (ESCI),
Год журнала:
2024,
Номер
unknown, С. 1 - 8
Опубликована: Март 5, 2024
Over
the
recent
decades,
world
has
witnessed
frequent
and
devastating
flood
events
with
loss
of
life
property.
The
focus
this
present
study
is
Panchaganga
River
basin
(PRB)
in
state
Maharashtra,
India,
chosen
as
site
for
susceptibility
mapping
(FSM).
Various
models,
including
frequency
ratio
(FR),
weight
evidence
(WoE),
Analytical
Hierarchy
Process
(AHP),
an
FA
model
(ensemble
FR
&
AHP),
were
employed
purpose.
To
facilitate
study,
a
comprehensive
inventory
comprising
200
historical
locations
was
prepared.
75
%
samples
selected
training
25
testing
model.
FSM
generated
categorized
into
five
zones
namely,
very
high,
moderate,
low,
no
risk
through
application
ArcGIS
software.
ROC
curves
plotted
validation
purpose
AUC
values
93.6
%,
91.6
95.5
FR,
WoE
respectively.
This
provides
valuable
insights
residents
living
near
River,
aiding
them
relocating
to
safer
areas
during
flooding
events.
Additionally,
farmers
PRB
can
employ
information
mitigate
various
losses
resulting
from
disasters.
International Journal of Disaster Risk Reduction,
Год журнала:
2024,
Номер
108, С. 104503 - 104503
Опубликована: Апрель 23, 2024
Floods
are
a
widespread
and
damaging
natural
phenomenon
that
causes
harm
to
human
lives,
resources,
property
has
agricultural,
eco-environmental,
economic
impacts.
Therefore,
it
is
crucial
perform
flood
susceptibility
mapping
(FSM)
identify
susceptible
zones
mitigate
reduce
damage.
This
study
assessed
the
damage
caused
by
2022
flash
in
Sindh
identified
flood-susceptible
based
on
frequency
ratio
(FR)
analytical
hierarchy
process
(AHP)
models.
Flood
inventory
maps
were
generated,
containing
150
sampling
points,
which
manually
selected
from
Landsat
imagery.
The
points
split
into
70%
for
training
30%
validating
results.
Furthermore,
fourteen
conditioning
factors
considered
analysis
developing
FSM.
final
FSM
categorized
five
zones,
representing
levels
very
low
high.
results
areas
under
high
Ghotki
(FR
4.42%
AHP
5.66%),
Dadu
21.40%
21.29%),
Sanghar
6.81%
6.78%).
Ultimately,
accuracy
was
evaluated
using
receiver
operating
characteristics
area
curve
method,
resulting
82%,
83%),
91%,
90%),
96%,
95%).
enhances
scientific
understanding
of
impacts
across
diverse
regions
emphasizes
importance
accurate
informed
decision-making.
findings
provide
valuable
insights
supportive
policymakers,
agricultural
planners,
stakeholders
engaged
risk
management
adverse
consequences
floods.
Abstract
Floods
are
natural
disasters
with
significant
economic
and
infrastructural
impacts.
Assessing
flood
susceptibility
in
mountainous
urban
regions
is
particularly
challenging
due
to
the
complicated
interaction
which
structures
terrain
affect
behavior.
This
study
employs
two
ensemble
machine
learning
algorithms,
Extreme
Gradient
Boosting
(XGBoost)
Random
Forest
(RF),
develop
maps
for
Hunza-Nagar
region,
has
been
experiencing
frequent
flooding
past
three
decades.
An
unsteady
flow
simulation
carried
out
HEC-RAS
utilizing
a
100-year
return
period
hydrograph
as
an
input
boundary
condition,
output
of
provided
spatial
inundation
extents
necessary
developing
inventory.
Ten
explanatory
factors,
including
climatic,
geological,
geomorphological
features
namely
elevation,
slope,
curvature,
topographic
wetness
index
(TWI),
normalized
difference
vegetation
(NDVI),
land
use
cover
(LULC),
rainfall,
lithology,
distance
roads
rivers
considered
mapping.
For
inventory,
random
sampling
technique
adopted
create
repository
non-flood
points,
incorporating
ten
geo-environmental
conditioning
factors.
The
models’
accuracy
assessed
using
area
under
curve
(AUC)
receiver
operating
characteristics
(ROC).
prediction
rate
AUC
values
0.912
RF
0.893
XGBoost,
also
demonstrating
superior
performance
accuracy,
precision,
recall,
F1-score,
kappa
evaluation
metrics.
Consequently,
model
selected
represent
map
area.
resulting
will
assist
national
disaster
management
infrastructure
development
authorities
identifying
high
susceptible
zones
carrying
early
mitigation
actions
future
floods.
Water,
Год журнала:
2025,
Номер
17(7), С. 937 - 937
Опубликована: Март 23, 2025
Flooding
is
among
the
most
destructive
natural
disasters
globally,
and
it
inflicts
severe
damage
on
both
environments
human-made
structures.
The
frequency
of
floods
has
been
increasing
due
to
unplanned
urbanization,
climate
change,
changes
in
land
use.
Flood
susceptibility
maps
help
identify
at-risk
areas,
supporting
informed
decisions
disaster
preparedness,
risk
management,
mitigation.
This
study
aims
generate
a
flood
map
for
Davidson
County
Tennessee
using
an
integrated
geographic
information
system
(GIS)
analytical
hierarchical
process
(AHP).
In
this
study,
ten
causative
factors
are
employed
flood-prone
zones.
AHP,
form
weighted
multi-criteria
decision
analysis,
applied
assess
relative
impact
weights
these
factors.
Subsequently,
into
ArcGIS
Pro
(Version
3.3)
create
area
overlay
approach.
resulting
classified
county
five
zones:
very
low
(17.48%),
(41.89%),
moderate
(37.53%),
high
(2.93%),
(0.17%).
FEMA
hazard
used
validate
created
from
Ultimately,
comparison
reinforced
accuracy
reliability
assessment
GIS
AHP
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
Quaternary Science Advances,
Год журнала:
2024,
Номер
15, С. 100210 - 100210
Опубликована: Июнь 24, 2024
Landslides
are
prevalent
in
the
Ethiopian
highlands,
particularly
east
Gojjam
zone,
which
is
highly
affected
by
landslide
problems.
This
research
was
carried
out
northwestern
Ethiopia.
The
study
area
part
of
an
economically
important
country,
and
it
main
source
water
for
Grand
Renaissance
Dam
(GERD).
objective
this
work
to
undertake
a
detailed
inventory
past
locations
prediction
present
future
hazards,
as
well
preparation
zonation
map
East
zone
using
Analytical
Hierarchy
Process
(AHP)
with
GIS
technique.
parameters
used
were
slope
degree,
aspect,
land
use
cover,
road
proximity,
rainfall,
lithology,
altitude,
river
proximity.
various
causative
collected
from
field,
suitable
modifications
made
thematic
maps.
Finally,
ratings
basis
prepare
LHZ
windows.
susceptibility
mapping
produced
environment.
results
show
that
driving
factors
hazards
manmade
activities.
Validation
revealed
more
than
80%
landslides
match
within
"high
hazard
zone"
reasonably
accepted
rationality
adopted
methodology.
considered
parameters,
their
evaluation
production
LHZ-Map,
confirmed.
very
urban
planners,
agricultural
studies,
environmentalists,
hazardous
prevention
mitigation
strategies.