Engineering Applications of Artificial Intelligence,
Journal Year:
2024,
Volume and Issue:
133, P. 108573 - 108573
Published: May 11, 2024
Understanding
the
effects
of
extreme
floods
on
critical
infrastructures
such
as
bridges
is
paramount
for
ensuring
safety
and
resilient
design
in
face
climate
change
events.
This
study
develops
robust
computational
predictive
modeling
tools
assessing
impacts
hydraulic
response
structural
resilience
bridges.
A
fluid
dynamic
(CFD)
model
utilizing
RANS
equations
k-ω
Shear
Stress
Transport
(SST)
simulating
supercritical
flows
adopted
to
compute
hydrodynamic
pressures
water
levels
bridge
piers
cylindrical
rectangular
shapes
during
a
flood
event.
The
CFD
validated
based
case
data
obtained
from
Haj
Omran
Bridge,
built
Khorramabad
River
Iran.
numerical
simulations
consider
hydrological
conditions
exclude
geotechnical
parameters
abutment
damages.
results
are
evaluated
well-established
guidelines.
Machine
learning
techniques,
including
Extreme
Gradient
Boosting
(XGBoost),
Random
Forest
(RF),
Support
Vector
Regression
(SVR),
optimized
with
Grid
Search
Cross-Validation
(GSCV),
enhance
accuracy
pressure
forecasting
at
piers.
XGBoost
exhibits
superior
performance
(R2
=
0.908,
RMSE
0.0279,
E
3.41%)
compared
RF
SVR
models.
All
estimated
by
falls
within
±6
percent
error
lines,
highlighting
model's
robustness
out-of-range
prediction.
Additionally,
an
Long
Short-Term
Memory
(LSTM)
effectively
predict
free
surface
flow
profiles
(i.e.
depth)
over
0.937
0.083),
demonstrating
its
potential
practical
applications
depth
predictions
infrastructures.
proposed
methodological
framework
outlined
this
can
facilitate
bridges,
enabling
assessment
Frontiers in Built Environment,
Journal Year:
2024,
Volume and Issue:
10
Published: Feb. 9, 2024
Seawalls
are
critical
defence
infrastructures
in
coastal
zones
that
protect
hinterland
areas
from
storm
surges,
wave
overtopping
and
soil
erosion
hazards.
Scouring
at
the
toe
of
sea
defences,
caused
by
wave-induced
accretion
bed
material
imposes
a
significant
threat
to
structural
integrity
infrastructures.
Accurate
prediction
scour
depths
is
essential
for
appropriate
efficient
design
maintenance
structures,
which
serve
mitigate
risks
failure
through
scouring.
However,
limited
guidance
predictive
tools
available
estimating
scouring
sloping
structures.
In
recent
years,
Artificial
Intelligence
Machine
Learning
(ML)
algorithms
have
gained
interest,
although
they
underpin
robust
models
many
engineering
applications,
such
yet
be
applied
prediction.
Here
we
develop
present
ML-based
predicting
seawall.
Four
ML
algorithms,
namely,
Random
Forest
(RF),
Gradient
Boosted
Decision
Trees
(GBDT),
Neural
Networks
(ANNs),
Support
Vector
Regression
(SVMR)
utilised.
Comprehensive
physical
modelling
measurement
data
utilised
validate
models.
A
Novel
framework
feature
selection,
importance,
hyperparameter
tuning
adopted
pre-
post-processing
steps
In-depth
statistical
analyses
proposed
evaluate
performance
The
results
indicate
minimum
80%
accuracy
across
all
tested
this
study
overall,
SVMR
produced
most
accurate
predictions
with
Coefficient
Determination
(
r
2
)
0.74
Mean
Absolute
Error
(MAE)
value
0.17.
algorithm
also
offered
computationally
among
tested.
methodological
can
datasets
rapid
assessment
facilitating
model-informed
decision-making.
Heliyon,
Journal Year:
2024,
Volume and Issue:
10(20), P. e37965 - e37965
Published: Sept. 30, 2024
Accurate
prediction
of
daily
river
flow
(Q
t
)
remains
a
challenging
yet
essential
task
in
hydrological
modeling,
particularly
crucial
for
flood
mitigation
and
water
resource
management.
This
study
introduces
an
advanced
M5
Prime
(M5P)
predictive
model
designed
to
estimate
Q
as
well
one-
two-day-ahead
forecasts
(i.e.
t+1
t+2
).
The
performance
M5P
ensembles
incorporating
Bootstrap
Aggregation
(BA),
Disjoint
Aggregating
(DA),
Additive
Regression
(AR),
Vote
(V),
Iterative
classifier
optimizer
(ICO),
Random
Subspace
(RS),
Rotation
Forest
(ROF)
were
comprehensively
evaluated.
proposed
models
applied
case
data
Tuolumne
County,
US,
using
dataset
comprising
measured
precipitation
(P
),
evaporation
(E
t),
.
A
wide
range
input
scenarios
explored
predicting
,
t+1,
t+2.
Results
indicate
that
P
significantly
influence
accuracy.
Notably,
relying
solely
on
the
most
correlated
variable
(e.g.,
t-1)
does
not
guarantee
robust
However,
extending
forecast
horizon
mitigates
low-correlation
variables
Performance
metrics
DA-M5P
achieves
superior
results,
with
Nash-Sutcliff
Efficiency
0.916
root
mean
square
error
23
m3/s,
followed
by
ROF-M5P,
BA-M5P,
AR-M5P,
RS-M5P,
V-M5P,
ICO-M5P,
standalone
model.
ensemble
modeling
framework
enhanced
capability
stand-alone
algorithm
1.2
%-22.6
%,
underscoring
its
efficacy
potential
advancing
forecasting.
Journal of Water Process Engineering,
Journal Year:
2024,
Volume and Issue:
63, P. 105512 - 105512
Published: May 24, 2024
Nitrous
oxide
(N2O)
is
a
significant
contributor
to
global
greenhouse
gas
emissions,
with
an
increasing
attention
for
its
mitigation.
Machine
learning
(ML)
models
hold
promise
as
alternative
mechanistic
in
N2O
prediction
from
wastewater
treatment
plants
(WWTPs).
Although
more
complex
ML
can
sometimes
enhance
performance,
they
may
also
yield
little
no
improvement.
Thus,
balancing
model
complexity
performance
essential
effective
models,
aspect
that
often
overlooked.
Carefully
these
elements
optimizing
efficacy
without
unnecessarily
complexity.
Hence,
this
study
exhaustively
investigates
the
broadly
adopted
hyperparameter
optimization
(HPO),
grid
search
optimization,
which
showed
limited
ability
consider
and
it
only
focuses
on
leading
potential
overfitting.
This
emphasizes
crucial
balance
between
presenting
new
algorithm
combines
input
feature
selection
HPO
efficiency
accuracy.
Consequently,
AdaBoost
achieved
same
accuracy
crafted
through
separate
HPO,
holding
R2
of
0.94
but
marginal
increase
RMSE
27.25
26.27.
It
simplified
by
using
fewer
estimators
shallower
trees,
thereby
lowering
risk
overfitting
suggesting
better
generalizability.
The
employs
multi-objective
NSGA-II
genetic
(GA),
outperforming
Nelder-Mead
algorithm.
approach
effectively
balances
accuracy,
enabling
development
computationally
efficient,
online
tools
emission
potentially
other
applications.
