Hydrological Sciences Journal,
Год журнала:
2021,
Номер
66(10), С. 1584 - 1596
Опубликована: Июнь 3, 2021
Accurate
prediction
of
dissolved
oxygen
(DO)
concentration
is
important
for
managing
healthy
aquatic
ecosystems.
This
study
investigates
the
comparative
potential
emotional
artificial
neural
network-genetic
algorithm
(EANN-GA),
and
three
ensemble
techniques,
i.e.
network
(EANN),
feedforward
(FFNN),
(NNE),
to
predict
DO
in
Kinta
River
basin
Malaysia.
The
performance
EANN-GA,
EANN,
FFNN,
NNE
models
predicting
was
evaluated
using
statistical
metrics
visual
interpretation.
Appraisal
results
revealed
a
promising
NNE-M3
model
(Nash-Sutcliffe
efficiency
(NSE)
=
0.8743/0.8630,
correlation
coefficient
(CC)
0.9351/0.9113,
mean
square
error
(MSE)
0.5757/0.6833
mg/L,
root
(RMSE)
0.7588/0.8266
absolute
percentage
(MAPE)
20.6581/14.1675)
during
calibration/validation
period
compared
FFNN
basin.
Landslides,
Год журнала:
2022,
Номер
19(10), С. 2489 - 2511
Опубликована: Июнь 30, 2022
Abstract
Recently,
integrated
machine
learning
(ML)
metaheuristic
algorithms,
such
as
the
artificial
bee
colony
(ABC)
algorithm,
genetic
algorithm
(GA),
gray
wolf
optimization
(GWO)
particle
swarm
(PSO)
and
water
cycle
(WCA),
have
become
predominant
approaches
for
landslide
displacement
prediction.
However,
these
algorithms
suffer
from
poor
reproducibility
across
replicate
cases.
In
this
study,
a
hybrid
approach
integrating
k-fold
cross
validation
(CV),
support
vector
regression
(SVR),
nonparametric
Friedman
test
is
proposed
to
enhance
reproducibility.
The
five
previously
mentioned
metaheuristics
were
compared
in
terms
of
accuracy,
computational
time,
robustness,
convergence.
results
obtained
Shuping
Baishuihe
landslides
demonstrate
that
can
be
utilized
determine
optimum
hyperparameters
present
statistical
significance,
thus
enhancing
accuracy
reliability
ML-based
Significant
differences
observed
among
metaheuristics.
Based
on
test,
which
was
performed
root
mean
square
error
(RMSE),
Kling-Gupta
efficiency
(KGE),
PSO
recommended
hyperparameter
tuning
SVR-based
prediction
due
its
ability
maintain
balance
between
precision,
robustness.
promising
presenting
Hydrology,
Год журнала:
2023,
Номер
10(3), С. 58 - 58
Опубликована: Фев. 27, 2023
Drought
monitoring
and
prediction
have
important
roles
in
various
aspects
of
hydrological
studies.
In
the
current
research,
standardized
precipitation
index
(SPI)
was
monitored
predicted
Peru
between
1990
2015.
The
study
proposed
a
hybrid
model,
called
ANN-FA,
for
SPI
time
scales
(SPI3,
SPI6,
SPI18,
SPI24).
A
state-of-the-art
firefly
algorithm
(FA)
has
been
documented
as
powerful
tool
to
support
modeling
issues.
ANN-FA
uses
an
artificial
neural
network
(ANN)
which
is
coupled
with
FA
Lima
via
other
stations.
Through
intelligent
utilization
series
from
neighbors’
stations
model
inputs,
suggested
approach
might
be
used
forecast
at
meteorological
station
insufficient
data.
To
conduct
this,
SPI3,
SPI24
were
modeled
using
stations’
datasets
Peru.
Various
error
criteria
employed
investigate
performance
model.
Results
showed
that
effective
promising
drought
also
multi-station
strategy
lack
results
can
help
predict
mean
absolute
=
0.22,
root
square
0.29,
Pearson
correlation
coefficient
0.94,
agreement
0.97
testing
phase
best
estimation
(SPI3).
Sustainability,
Год журнала:
2020,
Номер
12(21), С. 8932 - 8932
Опубликована: Окт. 27, 2020
Accurate
information
about
groundwater
level
prediction
is
crucial
for
effective
planning
and
management
of
resources.
In
the
present
study,
Artificial
Neural
Network
(ANN),
optimized
with
a
Genetic
Algorithm
(GA-ANN),
was
employed
seasonal
table
depth
(GWTD)
in
area
between
Ganga
Hindon
rivers
located
Uttar
Pradesh
State,
India.
A
total
18
models
both
seasons
(nine
pre-monsoon
nine
post-monsoon)
have
been
formulated
by
using
recharge
(GWR),
discharge
(GWD),
previous
data
from
21-year
period
(1994–2014).
The
hybrid
GA-ANN
models’
predictive
ability
evaluated
against
traditional
GA
based
on
statistical
indicators
visual
inspection.
results
appraisal
indicates
that
outperformed
predicting
GWTD
study
region.
Overall,
GA-ANN-8
model
an
8-9-1
structure
(i.e.,
8:
inputs,
9:
neurons
hidden
layer,
1:
output)
nominated
optimal
during
pre-
post-monsoon
seasons.
Additionally,
it
noted
maximum
number
input
variables
approach
improved
accuracy.
conclusion,
proposed
model’s
findings
could
be
readily
transferable
or
implemented
other
parts
world,
specifically
those
similar
geology
hydrogeology
conditions
sustainable
resources
management.
