Environmental Research Letters,
Год журнала:
2024,
Номер
19(7), С. 073003 - 073003
Опубликована: Июнь 3, 2024
Abstract
Flood
risk
in
urban
areas
will
increase
massively
under
future
urbanization
and
climate
change.
Urban
flood
models
have
been
increasingly
applied
to
assess
impacts
of
on
risk.
For
this
purpose,
different
methodological
approaches
developed
order
reflect
the
complexity
dynamics
growth.
To
state-of-the
art
application
scenarios,
we
conducted
a
structured
literature
review
systematically
analyzed
93
publications
with
141
case
studies.
Our
shows
that
hydrological
hydrodynamic
are
most
commonly
used
simulate
Future
is
mostly
considered
as
sprawl
through
adjustment
land
use
maps
roughness
parameters.
A
low
number
additionally
consider
transitions
structures
densification
processes
their
scenarios.
High-resolution
physically
based
advanced
well
suited
for
describing
quantifiable
data-rich
contexts.
In
regions
limited
data,
argue
reducing
level
detail
increasing
patterns
should
be
improve
quality
projections
urbanization.
also
call
development
integrative
model
such
causal
network
greater
explanatory
power
enable
processing
qualitative
data.
Ecological Indicators,
Год журнала:
2023,
Номер
156, С. 111137 - 111137
Опубликована: Окт. 29, 2023
Urban
flooding
risks,
often
overlooked
by
conventional
methods,
can
be
profoundly
affected
city
configurations.
However,
explainable
Artificial
Intelligence
could
provide
insights
into
how
urban
configurations
flooding.
This
study,
taking
entered
on
Shenzhen
City,
deploys
an
XGBoost,
integrating
SHapley
Additive
exPlanation
and
Partial
Dependency
Plots,
to
assess
morphology
influences
susceptibility.
The
models
strategies
presented
in
this
study
aimed
adapt
extreme
storms
from
the
perspective
of
spatial
configuration
planning.
findings
underscore
varying
impact
disaster
variables
flooding,
with
morphological
attributes
becoming
highly
significant
during
severe
inundations.
In
analysis,
mean
building
volume
emerged
as
a
pivotal
parameter,
SHAP
value
0.0107
m
contribution
ratio
9.70
%.
indicates
that
should
optimized
minimize
risks.
It
is
recommended
Mean
Building
Volume
(MBV)
maintained
within
range
1.25
km3
2.5
km3,
Standard
Deviation
(SDBV)
kept
below
2.814
km3.
By
harnessing
algorithms,
offers
intricate
relationship
between
forms
flood
risk,
thereby
informing
development
effective
adaptation
strategies.
Remote Sensing,
Год журнала:
2024,
Номер
16(2), С. 320 - 320
Опубликована: Янв. 12, 2024
Due
to
the
complex
interaction
of
urban
and
mountainous
floods,
assessing
flood
susceptibility
in
areas
presents
a
challenging
task
environmental
research
risk
analysis.
Data-driven
machine
learning
methods
can
evaluate
lacking
essential
hydrological
data,
utilizing
remote
sensing
data
limited
historical
inundation
records.
In
this
study,
two
ensemble
algorithms,
Random
Forest
(RF)
XGBoost,
were
adopted
assess
Kunming,
typical
area
prone
severe
disasters.
A
inventory
was
created
using
observations
from
2018
2022.
The
spatial
database
included
10
explanatory
factors,
encompassing
climatic,
geomorphic,
anthropogenic
factors.
Artificial
Neural
Network
(ANN)
Support
Vector
Machine
(SVM)
selected
for
model
comparison.
To
minimize
influence
expert
opinions
on
training,
study
employed
strategy
uniformly
random
sampling
historically
non-flooded
negative
sample
selection.
results
demonstrated
that
(1)
algorithms
offer
higher
accuracy
than
other
methods,
with
RF
achieving
highest
accuracy,
evidenced
by
an
under
curve
(AUC)
0.87,
followed
XGBoost
at
0.84,
surpassing
both
ANN
(0.83)
SVM
(0.82);
(2)
interpretability
highlighted
differences
potential
distribution
training
data’s
positive
samples.
Feature
importance
be
utilized
human
bias
collection
flooded-site
samples,
more
targeted
maps
area’s
road
network
obtained;
(3)
exhibited
greater
stability
robustness
datasets
varied
as
their
performance
F1-Score,
Kappa,
AUC
metrics.
This
paper
further
substantiates
superiority
assessment
tasks
perspectives
interpretability,
robustness,
enhances
understanding
impact
samples
such
assessments,
optimizes
specific
process
data-driven
methods.
Water,
Год журнала:
2023,
Номер
15(1), С. 178 - 178
Опубликована: Янв. 1, 2023
Many
urban
areas
in
tropical
Southeast
Asia,
e.g.,
Bangkok
Thailand,
have
recently
been
experiencing
unprecedentedly
intense
flash
floods
due
to
climate
change.
The
rapid
flood
inundation
has
caused
extremely
severe
damage
residents
and
social
infrastructures.
In
addition,
Asia
usually
inadequate
capacities
drainage
systems,
complicated
land
use
patterns,
a
large
vulnerable
population
limited
areas.
To
reduce
the
risk
enhance
resilience
of
communities,
it
essential
importance
develop
real-time
forecasting
systems
for
disaster
prevention
authorities
public.
This
paper
reviewed
state-of-the-art
models
floods.
system
basically
consists
following
subsystems,
i.e.,
rainfall
forecasting,
modelling,
area
mapping.
summarized
recent
radar
data
utilization
methods
physical-process-based
hydraulic
prediction,
data-driven
artificial
intelligence
(AI)
system.
also
dealt
with
available
technologies
digital
surface
(DSMs)
finer
terrain
systems.
review
indicated
that
an
obstacle
using
process-based
was
computational
resources
shorter
lead
time
many
Asia.
further
discussed
prospects
AI
Ecological Indicators,
Год журнала:
2024,
Номер
160, С. 111941 - 111941
Опубликована: Март 1, 2024
The
water
quality
of
urban
rivers
is
subject
to
fluctuation
caused
by
rainstorm
flood.
uncertainty
flooding
in
a
dynamic
environment
brings
about
changes
river
quality,
presenting
significant
challenge.
Water
samples
were
collected
at
7
sampling
sites
Handan
City
China
from
January
2020
August
2023,
and
9
parameters
(WT,
pH,
Cond,
Do,
COD,
BOD,
NH3-N,
TP,
TP)
analyzed.
Specifically,
the
spatial
temporal
variation
Qingzhang
River
was
In
terms
season,
concentration
spring
winter
found
be
significantly
higher
than
that
summer
autumn.
Spatially,
lower
upstream
compared
downstream.
Furthermore,
it
discovered
reservoir
had
purifying
effect
on
river.
Additionally,
comparison
during
flood
non-flood
periods
revealed
upstream,
midstream,
downstream
periods.
These
findings
indicated
that,
urbanization
factor,
hydrological
which
results
runoff
carrying
nutrients
into
River,
plays
crucial
role
change
its
quality.
Moreover,
autoregressive
integrated
moving
average
(ARIMA)
method
employed
create
pollution
emergency
prediction
model
for
different
sections
an
adaptive
purification
strategy
formulated
based
patterns.
research
contribute
theoretical
basis
management
resources.
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
