Biomimetics,
Год журнала:
2024,
Номер
9(12), С. 727 - 727
Опубликована: Ноя. 28, 2024
Beluga
whale
optimization
(BWO)
is
a
swarm-based
metaheuristic
algorithm
inspired
by
the
group
behavior
of
beluga
whales.
BWO
suffers
from
drawbacks
such
as
an
insufficient
exploration
capability
and
tendency
to
fall
into
local
optima.
To
address
these
shortcomings,
this
paper
proposes
augmented
multi-strategy
(AMBWO).
The
adaptive
population
learning
strategy
proposed
improve
global
BWO.
introduction
roulette
equilibrium
selection
allows
have
more
reference
points
choose
among
during
exploitation
phase,
which
enhances
flexibility
algorithm.
In
addition,
avoidance
improves
algorithm’s
ability
escape
optima
enriches
quality.
order
validate
performance
AMBWO,
extensive
evaluation
comparisons
with
other
state-of-the-art
improved
algorithms
were
conducted
on
CEC2017
CEC2022
test
sets.
Statistical
tests,
convergence
analysis,
stability
analysis
show
that
AMBWO
exhibits
superior
overall
performance.
Finally,
applicability
superiority
was
further
verified
several
engineering
problems.
Scientific Reports,
Год журнала:
2023,
Номер
13(1)
Опубликована: Ноя. 29, 2023
Fine
particulate
matter
(PM2.5)
is
a
significant
air
pollutant
that
drives
the
most
chronic
health
problems
and
premature
mortality
in
big
metropolitans
such
as
Delhi.
In
context,
accurate
prediction
of
PM2.5
concentration
critical
for
raising
public
awareness,
allowing
sensitive
populations
to
plan
ahead,
providing
governments
with
information
alerts.
This
study
applies
novel
hybridization
extreme
learning
machine
(ELM)
snake
optimization
algorithm
called
ELM-SO
model
forecast
concentrations.
The
has
been
developed
on
quality
inputs
meteorological
parameters.
Furthermore,
hybrid
compared
individual
models,
Support
Vector
Regression
(SVR),
Random
Forest
(RF),
Extreme
Learning
Machines
(ELM),
Gradient
Boosting
Regressor
(GBR),
XGBoost,
deep
known
Long
Short-Term
Memory
networks
(LSTM),
forecasting
results
suggested
exhibited
highest
level
predictive
performance
among
five
testing
value
squared
correlation
coefficient
(R2)
0.928,
root
mean
square
error
30.325
µg/m3.
study's
findings
suggest
technique
valuable
tool
accurately
concentrations
could
help
advance
field
forecasting.
By
developing
state-of-the-art
pollution
models
incorporate
ELM-SO,
it
may
be
possible
understand
better
anticipate
effects
human
environment.
Heliyon,
Год журнала:
2023,
Номер
10(1), С. e22942 - e22942
Опубликована: Ноя. 28, 2023
Drought
is
a
hazardous
natural
disaster
that
can
negatively
affect
the
environment,
water
resources,
agriculture,
and
economy.
Precise
drought
forecasting
trend
assessment
are
essential
for
management
to
reduce
detrimental
effects
of
drought.
However,
some
existing
modeling
techniques
have
limitations
hinder
precise
forecasting,
necessitating
exploration
suitable
approaches.
This
study
examines
two
models,
Long
Short-Term
Memory
(LSTM)
hybrid
model
integrating
regularized
extreme
learning
machine
Snake
algorithm,
forecast
hydrological
droughts
one
six
months
in
advance.
Using
Multivariate
Standardized
Streamflow
Index
(MSSI)
computed
from
58
years
streamflow
data
drier
Malaysian
stations,
models
were
compared
classical
such
as
gradient
boosting
regression
K-nearest
validation
purposes.
The
RELM-SO
outperformed
other
month
ahead
at
station
S1,
with
lower
root
mean
square
error
(RMSE
=
0.1453),
absolute
(MAE
0.1164),
higher
Nash-Sutcliffe
efficiency
index
(NSE
0.9012)
Willmott
(WI
0.9966).
Similarly,
S2,
had
0.1211
MAE
0.0909),
0.8941
WI
0.9960),
indicating
improved
accuracy
comparable
models.
Due
significant
autocorrelation
data,
traditional
statistical
metrics
may
be
inadequate
selecting
optimal
model.
Therefore,
this
introduced
novel
parameter
evaluate
model's
effectiveness
accurately
capturing
turning
points
data.
Accordingly,
significantly
19.32
%
21.52
when
LSTM.
Besides,
reliability
analysis
showed
was
most
accurate
providing
long-term
forecasts.
Additionally,
innovative
analysis,
an
effective
method,
used
analyze
trends.
revealed
October,
November,
December
experienced
occurrences
than
months.
research
advances
assessment,
valuable
insights
decision-making
drought-prone
regions.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Май 11, 2024
Abstract
Liquefaction
is
a
devastating
consequence
of
earthquakes
that
occurs
in
loose,
saturated
soil
deposits,
resulting
catastrophic
ground
failure.
Accurate
prediction
such
geotechnical
parameter
crucial
for
mitigating
hazards,
assessing
risks,
and
advancing
engineering.
This
study
introduces
novel
predictive
model
combines
Extreme
Learning
Machine
(ELM)
with
Dingo
Optimization
Algorithm
(DOA)
to
estimate
strain
energy-based
liquefaction
resistance.
The
hybrid
(ELM-DOA)
compared
the
classical
ELM,
Adaptive
Neuro-Fuzzy
Inference
System
Fuzzy
C-Means
(ANFIS-FCM
model),
Sub-clustering
(ANFIS-Sub
model).
Also,
two
data
pre-processing
scenarios
are
employed,
namely
traditional
linear
non-linear
normalization.
results
demonstrate
normalization
significantly
enhances
performance
all
models
by
approximately
25%
Furthermore,
ELM-DOA
achieves
most
accurate
predictions,
exhibiting
lowest
root
mean
square
error
(484.286
J/m
3
),
absolute
percentage
(24.900%),
(404.416
highest
correlation
determination
(0.935).
Additionally,
Graphical
User
Interface
(GUI)
has
been
developed,
specifically
tailored
model,
assist
engineers
researchers
maximizing
utilization
this
model.
GUI
provides
user-friendly
platform
easy
input
accessing
model's
enhancing
its
practical
applicability.
Overall,
strongly
support
proposed
serving
as
an
effective
tool
resistance
engineering,
aiding
predicting
hazards.
Abstract.
Hydrological
drought
is
one
of
the
main
hydroclimatic
hazards
worldwide,
affecting
water
availability,
ecosystems
and
socioeconomic
activities.
This
phenomenon
commonly
characterized
by
Standardized
Streamflow
Index
(SSI),
which
widely
used
because
its
straightforward
formulation
calculation.
Nevertheless,
there
limited
understanding
what
SSI
actually
reveals
about
how
climate
anomalies
propagate
through
terrestrial
cycle.
To
find
possible
explanations,
we
implemented
SUMMA
hydrological
model
coupled
with
mizuRoute
routing
in
six
hydroclimatically
different
case
study
basins
located
on
western
slopes
extratropical
Andes,
examined
correlations
between
(computed
from
models
for
1,
3
6-month
time
scales)
potential
explanatory
variables
–
including
precipitation
simulated
catchment-scale
storages
aggregated
at
scales.
Additionally,
analyzed
impacts
adopting
scales
propagation
analyses
specific
events
meteorological
to
soil
moisture
focus
their
duration
intensity.
The
results
reveal
that
choice
scale
has
larger
effects
rainfall-dominated
regimes
compared
snowmelt-driven
basins,
especially
when
fluxes
are
longer
than
9
months.
In
all
analyzed,
strongest
relationships
(Spearman
rank
correlation
values
over
0.7)
were
obtained
using
aggregations
compute
9–12
months
variables,
excepting
aquifer
storage
basins.
Finally,
show
trajectories
Precipitation
(SPI),
Soil
Moisture
(SSMI)
may
change
drastically
selection
scale.
Overall,
this
highlights
need
caution
selecting
standardized
indices
associated
scales,
since
event
characterizations,
monitoring
analyses.
Abstract.
Hydrological
drought
is
one
of
the
main
hydroclimatic
hazards
worldwide,
affecting
water
availability,
ecosystems
and
socioeconomic
activities.
This
phenomenon
commonly
characterized
by
Standardized
Streamflow
Index
(SSI),
which
widely
used
because
its
straightforward
formulation
calculation.
Nevertheless,
there
limited
understanding
what
SSI
actually
reveals
about
how
climate
anomalies
propagate
through
terrestrial
cycle.
To
find
possible
explanations,
we
implemented
SUMMA
hydrological
model
coupled
with
mizuRoute
routing
in
six
hydroclimatically
different
case
study
basins
located
on
western
slopes
extratropical
Andes,
examined
correlations
between
(computed
from
models
for
1,
3
6-month
time
scales)
potential
explanatory
variables
–
including
precipitation
simulated
catchment-scale
storages
aggregated
at
scales.
Additionally,
analyzed
impacts
adopting
scales
propagation
analyses
specific
events
meteorological
to
soil
moisture
focus
their
duration
intensity.
The
results
reveal
that
choice
scale
has
larger
effects
rainfall-dominated
regimes
compared
snowmelt-driven
basins,
especially
when
fluxes
are
longer
than
9
months.
In
all
analyzed,
strongest
relationships
(Spearman
rank
correlation
values
over
0.7)
were
obtained
using
aggregations
compute
9–12
months
variables,
excepting
aquifer
storage
basins.
Finally,
show
trajectories
Precipitation
(SPI),
Soil
Moisture
(SSMI)
may
change
drastically
selection
scale.
Overall,
this
highlights
need
caution
selecting
standardized
indices
associated
scales,
since
event
characterizations,
monitoring
analyses.