Water,
Год журнала:
2023,
Номер
15(6), С. 1149 - 1149
Опубликована: Март 15, 2023
The
estimation
of
reference
evapotranspiration
(ETo),
a
crucial
step
in
the
hydrologic
cycle,
is
essential
for
system
design
and
management,
including
balancing,
planning,
scheduling
agricultural
water
supply
resources.
When
climates
vary
from
arid
to
semi-arid,
there
are
problems
with
lack
meteorological
data
future
information
on
ETo,
as
case
Egypt,
it
more
important
estimate
ETo
precisely.
To
address
this,
current
study
aimed
model
Egypt’s
most
governorates
(Al
Buhayrah,
Alexandria,
Ismailiyah,
Minufiyah)
using
four
machine
learning
(ML)
algorithms:
linear
regression
(LR),
random
subspace
(RSS),
additive
(AR),
reduced
error
pruning
tree
(REPTree).
Climate
Forecast
System
Reanalysis
(CFSR)
National
Centers
Environmental
Prediction
(NCEP)
was
used
gather
daily
climate
variables
1979
2014.
datasets
were
split
into
two
sections:
training
phase,
i.e.,
1979–2006,
testing
2007–2014.
Maximum
temperature
(Tmax),
minimum
(Tmin),
solar
radiation
(SR)
found
be
three
input
that
had
influence
outcome
subset
sensitivity
analysis.
A
comparative
analysis
ML
models
revealed
REPTree
outperformed
competitors
by
achieving
best
values
various
performance
matrices
during
phases.
study’s
novelty
lies
use
predict
this
algorithm
has
not
been
commonly
purpose.
Given
sparse
attempts
such
research,
remarkable
accuracy
predicting
highlighted
rarity
study.
In
order
combat
effects
aridity
through
better
resource
also
cautions
authorities
concentrate
their
policymaking
adaptation.
Sustainability,
Год журнала:
2022,
Номер
14(13), С. 8209 - 8209
Опубликована: Июль 5, 2022
Nowadays,
great
attention
has
been
attributed
to
the
study
of
runoff
and
its
fluctuation
over
space
time.
There
is
a
crucial
need
for
good
soil
water
management
system
overcome
challenges
scarcity
other
natural
adverse
events
like
floods
landslides,
among
others.
Rainfall–runoff
(R-R)
modeling
an
appropriate
approach
prediction,
making
it
possible
take
preventive
measures
avoid
damage
caused
by
hazards
such
as
floods.
In
present
study,
several
data-driven
models,
namely,
multiple
linear
regression
(MLR),
adaptive
splines
(MARS),
support
vector
machine
(SVM),
random
forest
(RF),
were
used
rainfall–runoff
prediction
Gola
watershed,
located
in
south-eastern
part
Uttarakhand.
The
model
analysis
was
conducted
using
daily
rainfall
data
12
years
(2009
2020)
watershed.
first
80%
complete
train
model,
remaining
20%
testing
period.
performance
models
evaluated
based
on
coefficient
determination
(R2),
root
mean
square
error
(RMSE),
Nash–Sutcliffe
efficiency
(NSE),
percent
bias
(PBAIS)
indices.
addition
numerical
comparison,
evaluated.
Their
performances
graphical
plotting,
i.e.,
time-series
line
diagram,
scatter
plot,
violin
relative
Taylor
diagram
(TD).
comparison
results
revealed
that
four
heuristic
methods
gave
higher
accuracy
than
MLR
model.
Among
learning
RF
(RMSE
(m3/s),
R2,
NSE,
PBIAS
(%)
=
6.31,
0.96,
0.94,
−0.20
during
training
period,
respectively,
5.53,
0.95,
0.92,
respectively)
surpassed
MARS,
SVM,
forecasting
all
cases
studied.
outperformed
models’
periods.
It
can
be
summarized
best-in-class
delivers
strong
potential
Water,
Год журнала:
2023,
Номер
15(3), С. 486 - 486
Опубликована: Янв. 25, 2023
Modeling
potential
evapotranspiration
(ET0)
is
an
important
issue
for
water
resources
planning
and
management
projects
involving
droughts
flood
hazards.
Evapotranspiration,
one
of
the
main
components
hydrological
cycle,
highly
effective
in
drought
monitoring.
This
study
investigates
efficiency
two
machine-learning
methods,
random
vector
functional
link
(RVFL)
relevance
machine
(RVM),
improved
with
new
metaheuristic
algorithms,
quantum-based
avian
navigation
optimizer
algorithm
(QANA),
artificial
hummingbird
(AHA)
modeling
ET0
using
limited
climatic
data,
minimum
temperature,
maximum
extraterrestrial
radiation.
The
outcomes
hybrid
RVFL-AHA,
RVFL-QANA,
RVM-AHA,
RVM-QANA
models
compared
single
RVFL
RVM
models.
Various
input
combinations
three
data
split
scenarios
were
employed.
results
revealed
that
AHA
QANA
considerably
methods
ET0.
Considering
periodicity
component
radiation
as
inputs
prediction
accuracy
applied
methods.
Agricultural Water Management,
Год журнала:
2023,
Номер
283, С. 108302 - 108302
Опубликована: Апрель 14, 2023
Precise
evapotranspiration
(ET)
estimation
is
critical
for
agricultural
water
management,
particularly
in
water-stressed
developing
countries.
Vapor
Pressure
Deficit
one
of
the
ET
parameters
that
has
a
significant
impact
on
its
calculation
(VPD).
This
paper
forecasts
VPD
using
ensemble
learning-based
modeling
eight
different
regions
(Dakahliyah,
Gharbiyah,
Kafr
Elsheikh,
Dumyat,
Port
Said,
Ismailia,
Sharqiyah,
and
Qalubiyah)
Egypt.
In
this
study,
six
machine
learning
algorithms
were
used:
Linear
Regression
(LR),
Additive
regression
trees
(ART),
Random
SubSpace
(RSS),
Forest
(RF),
Reduced
Error
Pruning
Tree
(REPTree),
Quinlan's
M5
algorithm
(M5P).
Monthly
vapor
pressure
data
obtained
from
Japanese
55-year
Reanalysis
JRA-55
1958
to
2021.
The
dateset
been
divided
into
two
segments:
training
stage
(1958–2005)
testing
(2006–2021).
Five
statistical
measures
used
evaluate
model
performances:
Correlation
Coefficient
(CC),
Mean
Absolute
(MAE),
Root
Square
(RMSE),
Relative
absolute
error
(RAE),
Squared
(RRSE),
across
both
stages.
RF
outperformed
rest
models
[CC
=
0.9694;
MAE
0.0967;
RMSE
0.1252;
RAE
(%)
21.7297
RRSE
24.0356],
followed
closely
by
REPTree
RSS
models.
On
other
hand,
M5P
performance
remained
moderate
LR
AR
worst.
During
stage,
terms
(which
statistic),
study
recommended
future
hydro-climatological
studies
general,
deficit
prediction
particular.
enables
magnitudes
be
predicted,
alerting
authorities
administrators
involved
focus
their
policy-making
more
specific
pathways
toward
climate
adaptation.
