Water Resources Research,
Journal Year:
2025,
Volume and Issue:
61(4)
Published: April 1, 2025
Abstract
Urban
flood
monitoring
is
crucial
for
understanding
processes
and
implementing
management
strategies.
However,
current
systems
cannot
comprehensively
capture
urban
flooding
dynamics.
Here
we
explore
the
use
of
cutting‐edge
Large
Multimodal
Models
(LMMs)
to
estimate
floodwater
depth
from
ground‐level
images,
as
alternative
observational
approaches.
Evaluated
on
two
image
data
sets,
LMMs
generate
estimations
exhibiting
acceptable
concordance
ground
truth,
with
GPT‐4
achieving
highest
accuracy
0.65
a
Spearman
correlation
coefficient
0.88.
Furthermore,
combined
effect
complexity
textual
prompt
found
influence
LMMs'
performance.
Our
study
systematically
demonstrates,
first
time,
that
can
be
effective
tools
imaging‐based
monitoring,
enlarging
forecasting
model
calibration.
Sustainable Cities and Society,
Journal Year:
2022,
Volume and Issue:
88, P. 104307 - 104307
Published: Nov. 17, 2022
Increase
in
urban
flood
hazards
has
become
a
major
threat
to
cities,
causing
considerable
losses
of
life
and
the
economy.
To
improve
pre-disaster
strategies
mitigate
potential
losses,
it
is
important
make
susceptibility
assessments
carry
out
spatiotemporal
analyses.
In
this
study,
we
used
standard
deviation
ellipse
(SDE)
analyze
spatial
pattern
floods
find
area
interest
(AOI)
based
upon
related
social
media
data
that
were
collected
Chengdu
city,
China.
We
as
response
variable
selected
10
flood-influencing
factors
independent
variables.
estimated
model
using
Naïve
Bayes
(NB)
method.
The
results
show
events
are
concentrated
northeast-central
part
especially
around
city
center.
Results
checked
by
Receiver
Operating
Characteristic
(ROC)
curve,
showing
under
curve
(AUC)
was
equal
0.8299.
This
validation
result
confirmed
can
predict
with
satisfactory
accuracy.
map
center
provides
realistic
reference
for
monitoring
early
warning.
Journal of Hydrology,
Journal Year:
2024,
Volume and Issue:
629, P. 130639 - 130639
Published: Jan. 14, 2024
The
major
concern
of
applying
citizen
science
in
water
resources
is
the
quality
data.
However,
there
are
limited
scientific
studies
addressing
this
and
showing
data
value.
In
study,
we
established
a
program
Akaki
catchment
which
hosts
Addis
Ababa,
Ethiopia.
Citizen
scientists
monitored
river
stage
at
multiple
gauging
sites
for
years.
We
evaluated
through
systematic
control.
Reference
was
obtained
from
neighboring
stations
professionals
while
evaluation
involved
graphical
inspections
statistical
methods.
quality-controlled
were
applied
to
evaluate
spatial
temporal
variation
rainfall-runoff
relationships.
Initially,
large
numbers
suspicious
detected
using
single
station
but
that
significantly
reduced
when
compared.
Further
comparison
against
professional
revealed
excellent
agreement
with
high
correlation
coefficient
(r
>
0.95),
low
centered
root
mean
square
error
(RMSE)
<
0.03-0.08
mm.
indicated
difference
relationship
over
dominantly
urban
rural
sub-catchments.
allowed
runoff
base
flow
index
recent
historical
periods
where
streamflow
unavailable
formal
source.
This
study
illustrates
immense
value
(i)
assessment
steps
building
confidence
on
data,
(ii)
enhancing
our
understanding
relationships
change
rapidly
urbanizing
catchment.
Ocean-Land-Atmosphere Research,
Journal Year:
2023,
Volume and Issue:
2
Published: Jan. 1, 2023
Coastal
areas
are
highly
vulnerable
to
flood
risks,
which
exacerbated
by
the
changing
climate.
This
paper
provides
a
comprehensive
review
of
literature
on
coastal
risk
assessment
and
resilience
evaluation
proposes
smart-resilient
city
framework
based
pre-disaster,
mid-disaster,
post-disaster
evaluations.
First,
this
systematically
reviews
origin
concept
development
resilience.
Next,
it
introduces
social-acceptable
criteria
level
for
different
phases.
Then,
management
system
smart
cities
is
proposed,
covering
3
phases
disasters
(before,
during,
after).
Risk
essential
in
pre-disaster
scenarios
because
understanding
potential
hazards
vulnerabilities
an
area
or
system.
Big
data
monitoring
during
component
effective
emergency
response
that
can
allow
more
informed
decisions
thus
quicker,
responses
disasters,
ultimately
saving
lives
minimizing
damage.
Data-informed
loss
assessments
crucial
providing
rapid,
accurate
impact.
understanding,
turn,
instrumental
expediting
recovery
reconstruction
efforts
aiding
decision-making
processes
resource
allocation.
Finally,
impacts
climate
change
summarized.
The
resilient
communities
better
equipped
withstand
adapt
environmental
conditions
crucial.
To
address
compound
floods,
researchers
should
focus
trigging
factor
interactions,
assessing
economic
social
improving
systems,
promoting
interdisciplinary
research
with
openness.
These
strategies
will
enable
holistic
risks
context
change.
Environmental Modelling & Software,
Journal Year:
2023,
Volume and Issue:
173, P. 105939 - 105939
Published: Dec. 27, 2023
This
study
explores
the
use
of
Deep
Convolutional
Neural
Network
(DCNN)
for
semantic
segmentation
flood
images.
Imagery
datasets
urban
flooding
were
used
to
train
two
DCNN-based
models,
and
camera
images
test
application
models
with
real-world
data.
Validation
results
show
that
both
extracted
extent
a
mean
F1-score
over
0.9.
The
factors
affected
performance
included
still
water
surface
specular
reflection,
wet
road
surface,
low
illumination.
In
testing,
reduced
visibility
during
storm
raindrops
on
surveillance
cameras
major
problems
extent.
High-definition
web
can
be
an
alternative
tool
trained
data
it
collected.
conclusion,
extract
from
flooding.
challenges
using
these
identified
through
this
research
present
opportunities
future
research.
Water Resources Research,
Journal Year:
2024,
Volume and Issue:
60(5)
Published: May 1, 2024
Abstract
Flooding
is
one
of
the
most
dangerous
and
costly
natural
hazards,
has
a
large
impact
on
infrastructure,
mobility,
public
health,
safety.
Despite
disruptive
impacts
flooding
predictions
increased
due
to
climate
change,
municipalities
have
little
quantitative
data
available
occurrence,
frequency,
or
extent
urban
floods.
To
address
this,
we
been
designing,
building,
deploying
low‐cost,
ultrasonic
sensors
systematically
collect
presence,
depth,
duration
street‐level
floods
in
New
York
City
(NYC),
through
project
called
FloodNet.
FloodNet
partnership
between
academic
researchers
NYC
municipal
agencies,
working
consultation
with
residents
community
organizations.
are
designed
be
compact,
rugged,
deployed
manner
that
independent
existing
power
network
infrastructure.
These
requirements
were
implemented
allow
deployment
hyperlocal,
city‐wide
sensor
network,
given
often
occur
distributed
local
variations
land
development,
population
density,
sewer
design,
topology.
Thus
far,
87
installed
across
five
boroughs
NYC.
recorded
flood
events
caused
by
high
tides,
stormwater
runoff,
storm
surge,
extreme
precipitation
events,
illustrating
feasibility
collecting
can
used
multiple
stakeholders
for
resiliency
planning
emergency
response.
