International Journal of Applied Earth Observation and Geoinformation,
Год журнала:
2023,
Номер
122, С. 103401 - 103401
Опубликована: Июль 14, 2023
Flash
floods
are
among
the
world
most
destructive
natural
disasters,
and
developing
optimum
hybrid
Machine
Learning
(ML)
models
for
flash
flood
susceptibility
(FFS)
modeling
remains
a
challenge.
This
study
proposed
novel
intelligence
algorithms
based
on
of
several
ensemble
ML
(i.e.,
Bagged
Flexible
Discriminant
Analysis
(BAFDA),
Extreme
Gradient
Boosting
(XBG),
Rotation
Forest
(ROF)
Boosted
Generalized
Additive
Model
(BGAM))
wrapper-based
factor
optimization
Recursive
Feature
Elimination
(RFE)
Boruta)
to
improve
accuracy
FFS
mapping
at
Neka-Haraz
watershed
in
Iran.
In
addition,
Random
Search
(RS)
method
is
meta-optimization
developed
hyper-parameters.
considers
20
conditioning
factors
(CgFs)
380
non-flood
locations
create
geospatial
database.
The
performance
each
model
was
evaluated
by
area
under
receiver
operating
characteristic
(ROC)
curve
(AUC)
validation
methods,
such
as
efficiency.
demonstrated
good
performance,
with
BGAM-Boruta
achieving
highest
(AUC
=
0.953,
Efficiency
0.910),
followed
ROF-Boruta
0.952),
ROF-RFE
0.951),
BAFDA-Boruta
0.950),
BGAM-RFE
ROF
0.949),
BGAM
0.948),
BAFDA-RFE
0.943),
XGB-Boruta
BAFDA
0.939),
XGB-RFE
0.938)
XGB
0.911).
model,
regional
coverage
about
46%
high
very
areas.
Moreover,
revealed
that
distance
river,
slope,
rainfall,
altitude,
road
CgFs
significant
this
region.
Applied Sciences,
Год журнала:
2023,
Номер
13(22), С. 12147 - 12147
Опубликована: Ноя. 8, 2023
This
paper
offers
a
comprehensive
overview
of
machine
learning
(ML)
methodologies
and
algorithms,
highlighting
their
practical
applications
in
the
critical
domain
water
resource
management.
Environmental
issues,
such
as
climate
change
ecosystem
destruction,
pose
significant
threats
to
humanity
planet.
Addressing
these
challenges
necessitates
sustainable
management
increased
efficiency.
Artificial
intelligence
(AI)
ML
technologies
present
promising
solutions
this
regard.
By
harnessing
AI
ML,
we
can
collect
analyze
vast
amounts
data
from
diverse
sources,
remote
sensing,
smart
sensors,
social
media.
enables
real-time
monitoring
decision
making
applications,
including
irrigation
optimization,
quality
monitoring,
flood
forecasting,
demand
enhance
agricultural
practices,
distribution
models,
desalination
plants.
Furthermore,
facilitates
integration,
supports
decision-making
processes,
enhances
overall
sustainability.
However,
wider
adoption
faces
challenges,
heterogeneity,
stakeholder
education,
high
costs.
To
provide
an
management,
research
focuses
on
core
fundamentals,
major
(prediction,
clustering,
reinforcement
learning),
ongoing
issues
offer
new
insights.
More
specifically,
after
in-depth
illustration
algorithmic
taxonomy,
comparative
mapping
all
specific
tasks.
At
same
time,
include
tabulation
works
along
with
some
concrete,
yet
compact,
descriptions
objectives
at
hand.
leveraging
tools,
develop
plans
address
world’s
supply
concerns
effectively.
Expert Systems with Applications,
Год журнала:
2023,
Номер
236, С. 121426 - 121426
Опубликована: Сен. 4, 2023
Cutting-edge
flood
visualisation
technologies
are
becoming
increasingly
important
in
managing
urban
risks,
particularly
from
the
perspective
of
stakeholders
who
play
a
crucial
role
controlling
and
reducing
risks
associated
with
events.
This
review
study
provides
comprehensive
overview
stakeholder
analysis
this
context,
highlighting
gaps
current
research
paving
way
for
future
investigations.
For
purpose,
scientific
literature
critical
conducted
based
on
identified
relevant
works
to
map
mutual
context.
categorises
cutting-edge
into
four
groups
-
virtual
reality,
augmented
mixed
digital
twin
explores
their
adoption
engaging
various
across
five
key
stages
risk
management:
prevention,
mitigation,
preparation,
response,
recovery.
Results
show
that
existing
has
primarily
concentrated
support
water
utilities
communication
general
public.
However,
there
is
noticeable
gap
regarding
engagement
such
as
policy-makers,
researchers,
insurance
providers.
Furthermore,
highlights
disparities
involvement
damage
assessment
studies,
lack
representation
policy-makers
researchers.
Finally,
introduces
concept
overlooked
interconnected
impacts
they
have,
which
received
relatively
little
attention
previous
research.
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.
