Water,
Journal Year:
2024,
Volume and Issue:
16(4), P. 607 - 607
Published: Feb. 18, 2024
River
water-level
prediction
is
crucial
for
mitigating
flood
damage
caused
by
torrential
rainfall.
In
this
paper,
we
attempt
to
predict
river
water
levels
using
a
deep
learning
model
based
on
radar
rainfall
data
instead
of
from
upstream
hydrological
stations.
A
incorporating
two-dimensional
convolutional
neural
network
(2D-CNN)
and
long
short-term
memory
(LSTM)
constructed
exploit
geographical
temporal
features
data,
transfer
method
newly
defined
flow–distance
matrix
presented.
The
results
our
evaluation
the
Oyodo
basin
in
Japan
show
that
presented
measurements
has
good
accuracy
case
rain,
with
Nash–Sutcliffe
efficiency
(NSE)
value
0.86
Kling–Gupta
(KGE)
0.83
6-h-ahead
forecast
top-four
peak
height
cases,
which
comparable
conventional
(NSE
=
0.84
KGE
0.83).
It
also
confirmed
maintains
its
performance
even
when
amount
training
site
reduced;
values
NSE
0.82
were
achieved
reducing
torrential-rain-period
12
3
periods
(with
105
other
rivers
learning).
demonstrate
few
rain
at
location
potentially
enable
us
if
stations
have
not
been
installed
location.
Hydrology,
Journal Year:
2023,
Volume and Issue:
10(7), P. 141 - 141
Published: June 30, 2023
As
one
of
nature’s
most
destructive
calamities,
floods
cause
fatalities,
property
destruction,
and
infrastructure
damage,
affecting
millions
people
worldwide.
Due
to
its
ability
accurately
anticipate
successfully
mitigate
the
effects
floods,
flood
modeling
is
an
important
approach
in
control.
This
study
provides
a
thorough
summary
modeling’s
current
condition,
problems,
probable
future
directions.
The
includes
models
based
on
hydrologic,
hydraulic,
numerical,
rainfall–runoff,
remote
sensing
GIS,
artificial
intelligence
machine
learning,
multiple-criteria
decision
analysis.
Additionally,
it
covers
heuristic
metaheuristic
techniques
employed
evaluation
examines
advantages
disadvantages
various
models,
evaluates
how
well
they
are
able
predict
course
impacts
floods.
constraints
data,
unpredictable
nature
model,
complexity
model
some
difficulties
that
must
overcome.
In
study’s
conclusion,
prospects
for
development
advancement
field
discussed,
including
use
advanced
technologies
integrated
models.
To
improve
risk
management
lessen
society,
report
emphasizes
necessity
ongoing
research
modeling.
Ecological Indicators,
Journal Year:
2023,
Volume and Issue:
156, P. 111137 - 111137
Published: Oct. 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.
AIMS Public Health,
Journal Year:
2024,
Volume and Issue:
11(1), P. 58 - 109
Published: Jan. 1, 2024
<abstract>
<p>In
recent
years,
machine
learning
(ML)
and
deep
(DL)
have
been
the
leading
approaches
to
solving
various
challenges,
such
as
disease
predictions,
drug
discovery,
medical
image
analysis,
etc.,
in
intelligent
healthcare
applications.
Further,
given
current
progress
fields
of
ML
DL,
there
exists
promising
potential
for
both
provide
support
realm
healthcare.
This
study
offered
an
exhaustive
survey
on
DL
system,
concentrating
vital
state
art
features,
integration
benefits,
applications,
prospects
future
guidelines.
To
conduct
research,
we
found
most
prominent
journal
conference
databases
using
distinct
keywords
discover
scholarly
consequences.
First,
furnished
along
with
cutting-edge
ML-DL-based
analysis
smart
a
compendious
manner.
Next,
integrated
advancement
services
including
ML-healthcare,
DL-healthcare,
ML-DL-healthcare.
We
then
DL-based
applications
industry.
Eventually,
emphasized
research
disputes
recommendations
further
studies
based
our
observations.</p>
</abstract>
Water Research,
Journal Year:
2024,
Volume and Issue:
252, P. 121202 - 121202
Published: Jan. 24, 2024
Hydrodynamic
models
can
accurately
simulate
flood
inundation
but
are
limited
by
their
high
computational
demand
that
scales
non-linearly
with
model
complexity,
resolution,
and
domain
size.
Therefore,
it
is
often
not
feasible
to
use
high-resolution
hydrodynamic
for
real-time
predictions
or
when
a
large
number
of
needed
probabilistic
design.
Computationally
efficient
surrogate
have
been
developed
address
this
issue.
The
recently
Low-fidelity,
Spatial
analysis,
Gaussian
Process
Learning
(LSG)
has
shown
strong
performance
in
both
efficiency
simulation
accuracy.
LSG
physics-guided
simulates
first
using
an
extremely
coarse
simplified
(i.e.
low-fidelity)
provide
initial
estimate
inundation.
Then,
the
low-fidelity
upskilled
via
Empirical
Orthogonal
Functions
(EOF)
analysis
Sparse
accurate
predictions.
Despite
promising
results
achieved
thus
far,
benchmarked
against
other
models.
Such
comparison
fully
understand
value
guidance
future
research
efforts
simulation.
This
study
compares
four
state-of-the-art
assessed
ability
temporal
spatial
evolution
events
within
beyond
range
used
training.
evaluated
three
distinct
case
studies
Australia
United
Kingdom.
found
be
superior
accuracy
extent
water
depth,
including
applied
outside
training
data
used,
while
achieving
efficiency.
In
addition,
play
crucial
role
overall
model.
IEEE Access,
Journal Year:
2023,
Volume and Issue:
11, P. 24472 - 24483
Published: Jan. 1, 2023
Floods
are
one
of
the
most
common
natural
disasters
that
occur
frequently
causing
massive
damage
to
property,
agriculture,
economy
and
life.
Flood
prediction
offers
a
huge
challenge
for
researchers
struggling
predict
floods
since
long
time.
In
this
article,
flood
forecasting
model
using
federated
learning
technique
has
been
proposed.
Federated
Learning
is
advanced
machine
(ML)
guarantees
data
privacy,
ensures
availability,
promises
security,
handles
network
latency
trials
inherent
in
by
prohibiting
be
transferred
over
training.
urges
onsite
training
local
models,
focuses
on
transmission
these
models
instead
sending
set
towards
central
server
aggregation
global
at
server.
proposed
integrates
locally
trained
eighteen
clients,
investigates
which
station
flooding
about
happen
generates
alert
specific
client
with
five
days
lead
A
feed
forward
neural
(FFNN)
where
expected.
module
FFNN
predicts
expected
water
level
taking
multiple
regional
parameters
as
input.
The
dataset
different
rivers
barrages
collected
from
2015
2021
considering
four
aspects
including
snow
melting,
rainfall-runoff,
flow
routing
hydrodynamics.
successfully
predicted
previous
happened
selected
zone
during
2010
84
%
accuracy.
