A Systematic Review of Urban Flood Susceptibility Mapping: Remote Sensing, Machine Learning, and Other Modeling Approaches
Remote Sensing,
Год журнала:
2025,
Номер
17(3), С. 524 - 524
Опубликована: Фев. 3, 2025
Climate
change
has
led
to
an
increase
in
global
temperature
and
frequent
intense
precipitation,
resulting
a
rise
severe
urban
flooding
worldwide.
This
growing
threat
is
exacerbated
by
rapid
urbanization,
impervious
surface
expansion,
overwhelmed
drainage
systems,
particularly
regions.
As
becomes
more
catastrophic
causes
significant
environmental
property
damage,
there
urgent
need
understand
address
flood
susceptibility
mitigate
future
damage.
review
aims
evaluate
remote
sensing
datasets
key
parameters
influencing
provide
comprehensive
overview
of
the
causative
factors
utilized
mapping.
also
highlights
evolution
traditional,
data-driven,
big
data,
GISs
(geographic
information
systems),
machine
learning
approaches
discusses
advantages
limitations
different
mapping
approaches.
By
evaluating
challenges
associated
with
current
practices,
this
paper
offers
insights
into
directions
for
improving
management
strategies.
Understanding
identifying
foundation
developing
effective
resilient
practices
will
be
beneficial
mitigating
Язык: Английский
Assessment of urban flood susceptibility based on a novel integrated machine learning method
Environmental Monitoring and Assessment,
Год журнала:
2024,
Номер
197(1)
Опубликована: Дек. 5, 2024
Язык: Английский
Sediment production susceptibility index in urban area: a case study of Campo Grande – MS, Brazil
RBRH,
Год журнала:
2024,
Номер
29
Опубликована: Янв. 1, 2024
ABSTRACT
Inadequate
urban
planning
has
contributed
to
the
sediment
production
in
Urban
Hydrographic
Micro-basins
(UHMs).
The
present
study
aims
develop
and
apply
Sediment
Production
Susceptibility
Index
(SPSI)
UHMs
from
Campo
Grande
–
Mato
Grosso
do
Sul
(MS),
Brazil,
based
on
Analysis
Hierarchical
Process
(AHP)
Geographic
Information
System
(GIS)
aggregation.
indicators
selected
for
composition
of
SPSI
are
Soil
Class
(49%),
Average
Slope
(22%),
Vegetation
Cover
(13%),
Unpaved
Streets
(16%).
It
is
essentially
jointly
analyze
both
spheres
(natural
anthropogenic)
obtain
greater
reliability
studies
related
sedimentation
areas.
undergoing
urbanization
more
susceptible
than
that
already
densely
occupied.
can
assist
public
managers
environmental
adoption
preventive
measures
against
silting
water
bodies
obstruction
drainage
systems.
Язык: Английский