Evapotranspiration
is
an
essential
component
of
the
hydrological
cycle.
Forecasting
reference
crop
evapotranspiration
(ETo)
using
a
reliable
and
generalized
framework
crucial
for
agricultural
operations,
especially
irrigation.
This
study
was
aimed
at
evaluating
performance
multivariate-multitemporal
intelligent
system
including
K-Best
selection
(KBest),
multivariate
variational
mode
decomposition
(MVMD),
cascade
forward
neural
network
(CFNN)
1-,
3-,
7-,
10-day-ahead
forecasting
daily
ETo
in
twelve
stations
California,
one
significant
regions
U.S.
The
input
variables
included
solar
radiation,
maximum
temperature,
minimum
average
dew
point,
vapor
pressure,
relative
humidity.
analysis
covered
span
20
years,
from
2003
to
2022.
In
additional
CFNN,
two
other
machine
learning
models,
namely,
extreme
(ELM)
bagging
regression
tree
(BRT),
were
integrated
with
various
preprocessing
techniques
construct
three
hybrid
i.e.,
MVMD-KBest-CFNN,
MVMD-KBest-ELM,
MVMD-KBest-BRT.
Using
MVMD
technique,
antecedent
information
features
factorized
into
intrinsic
functions
residuals.
Subsequently,
most
influential
sub-components
filtered
KBest
reduce
computational
cost
enhance
accuracy
before
inputting
models.
Several
statistical
indices,
such
as
correlation
coefficient
(R)
root
mean
square
error
(RMSE),
used
addition
diagnostic
validation
methods
assess
robustness
frameworks
standalone
According
results
obtained
testing
phase,
averaged
across
all
stations,
MVMD-KBest-CFNN
MVMD-KBest-ELM
models
outperformed
MVMD-KBest-BRT
model,
R
values
0.983,
0.980,
0.977,
0.968
forecasts,
respectively.
corresponding
RMSE
0.390,
0.416,
0.450,
0.517
mm/d,
demonstrating
commendable
prediction
even
longer
lead
times.
Ecological Indicators,
Journal Year:
2023,
Volume and Issue:
153, P. 110478 - 110478
Published: June 13, 2023
Forecasting
accurately
suspended
sediment
load
(SSL)
in
the
basin
is
one
of
most
critical
issues
for
river
engineering,
environment,
and
water
resources
management
which
effectively
reduces
flood
damages.
In
this
study,
a
new
multi-criteria
hybrid
expert
system
comprised
empirical
wavelet
decomposition
(EWT)
integrated
with
Encoder-Decoder
Bidirectional
long
short-term
memory
(EDBi-LSTM),
supported
by
five
feature
selection
(FS)
methods
was
developed
first
time
to
forecast
daily
SSL
at
two
study
sites
(Bamini
Ashti)
Godavari
basin,
India.
The
employed
FS
schemes
are
including
Boruta-Random
forest
(BRF),
simulated
annealing
(SA),
Relief
algorithm,
Ridge
regression
(RR),
Mutual
information
(MI)
where
BRF
coupled
EWT
EDBi-LSTM
(i.e.,
EWT-EDBi-LSTM-Boruta)
identified
as
main
forecasting
paradigm.
Here
original
signals
monsoon
season
(2001–2015)
only
input
were
considered
events
scale
both
zones.
decomposed
using
technique
considering
significant
antecedent
time-lagged
inputs
based
on
partial
auto-correlation
function
(PACF).
next
stage,
strategies
addressed
specify
sub-sequences
reduce
computational
cost
enhance
accuracy.
Besides,
extreme
gradient
boosting
(XGB)
approach
implemented
compare
potential
standalone
counterpart
models
sites.
According
several
goodness-of-fit
indices
validation
tools,
outcomes
Bamini
Ashti
demonstrated
that
EWT-EDBi-LSTM-Boruta
model,
achieved
best
accuracy,
followed
EWT-XGB-Boruta,
EWT-EDBi-LSTM-SA,
EWT-XGB-SA,
respectively.
Comparing
all
showed
BRF,
SA,
RR
performed
better
integration
machine
learning
(ML)
models.
Heliyon,
Journal Year:
2024,
Volume and Issue:
10(12), P. e32572 - e32572
Published: June 1, 2024
This
study
explores
the
influence
of
social
media
content
on
societal
attitudes
and
actions
during
critical
events,
with
a
special
focus
occurrences
in
Chile,
such
as
COVID-19
pandemic,
2019
protests,
wildfires
2017
2023.
By
leveraging
novel
tweet
dataset,
this
introduces
new
metrics
for
assessing
sentiment,
inclusivity,
engagement,
impact,
thereby
providing
comprehensive
framework
analyzing
dynamics.
The
methodology
employed
enhances
sentiment
classification
through
use
Deep
Random
Vector
Functional
Link
(D-RVFL)
neural
network,
which
demonstrates
superior
performance
over
traditional
models
Support
Machines
(SVM),
naive
Bayes,
back
propagation
(BP)
networks,
achieving
an
overall
average
accuracy
78.30%
(0.17).
advancement
is
attributed
to
deep
learning
techniques
direct
input-output
connections
that
facilitate
faster
more
precise
classification.
analysis
differentiates
roles
influencers,
press
radio,
television
handlers
crises,
revealing
how
various
actors
affect
information
dissemination
audience
engagement.
dissecting
online
behaviors
classifying
sentiments
using
RVFL
sheds
light
effects
digital
landscape
emergencies.
These
findings
underscore
importance
understanding
nuances
engagement
develop
effective
crisis
communication
strategies.
