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.
The
year
2022
was
the
driest
in
Portugal
since
1931
with
97%
of
territory
severe
drought.Water
is
especially
important
for
agricultural
sector
Portugal,
as
it
represents
78%
total
consumption
according
to
Water
Footprint
report
published
2010.Reference
evapotranspiration
essential
due
its
importance
optimal
irrigation
planning
that
reduces
water
consumption.This
study
analyzes
and
proposes
a
framework
forecast
daily
reference
at
eight
stations
from
2012
without
relying
on
public
meteorological
forecasts.The
data
include
obtained
sensors
included
stations.The
goal
perform
multi-horizon
forecasting
using
multiple
related
covariates.The
combines
processing
analysis
several
state-of-the-art
methods
including
classical,
linear,
tree-based,
artificial
neural
network
ensembles.Then,
an
ensemble
all
trained
models
proposed
recent
bioinspired
metaheuristic
named
Coronavirus
Optimization
Algorithm
weight
predictions.The
results
terms
MAE
MSE
are
reported,
indicating
our
approach
achieved
0.658.
Heliyon,
Год журнала:
2023,
Номер
9(5), С. e16290 - e16290
Опубликована: Май 1, 2023
Knowledge
of
the
stage-discharge
rating
curve
is
useful
in
designing
and
planning
flood
warnings;
thus,
developing
a
reliable
fundamental
crucial
component
water
resource
system
engineering.
Since
continuous
measurement
often
impossible,
relationship
generally
used
natural
streams
to
estimate
discharge.
This
paper
aims
optimize
using
generalized
reduced
gradient
(GRG)
solver
test
accuracy
applicability
hybridized
linear
regression
(LR)
with
other
machine
learning
techniques,
namely,
regression-random
subspace
(LR-RSS),
regression-reduced
error
pruning
tree
(LR-REPTree),
regression-support
vector
(LR-SVM)
regression-M5
pruned
(LR-M5P)
models.
An
application
these
hybrid
models
was
performed
modeling
Gaula
Barrage
problem.
For
this,
12-year
historical
data
were
collected
analyzed.
The
daily
flow
(m3/s)
stage
(m)
from
during
monsoon
season,
i.e.,
June
October
only
03/06/2007
31/10/2018,
for
discharge
simulation.
best
suitable
combination
input
variables
LR,
LR-RSS,
LR-REPTree,
LR-SVM,
LR-M5P
identified
decided
gamma
test.
GRG-based
equations
found
be
as
effective
more
accurate
conventional
equations.
outcomes
GRG,
compared
observed
values
based
on
Nash
Sutcliffe
model
efficiency
coefficient
(NSE),
Willmott
Index
Agreement
(d),
Kling-Gupta
(KGE),
mean
absolute
(MAE),
bias
(MBE),
relative
percent
(RE),
root
square
(RMSE)
Pearson
correlation
(PCC)
determination
(R2).
LR-REPTree
(combination
1:
NSE
=
0.993,
d
0.998,
KGE
0.987,
PCC(r)
0.997,
R2
0.994
minimum
value
RMSE
0.109,
MAE
0.041,
MBE
−0.010
RE
−0.1%;
2;
0.941,
0.984,
0.
923,
973,
947
331,
0.143,
−0.089
−0.9%)
superior
all
combinations
testing
period.
It
also
noticed
that
performance
alone
LR
its
(i.e.,
LR-M5P)
better
than
curve,
including
GRG
method.
Applied Water Science,
Год журнала:
2024,
Номер
14(7)
Опубликована: Июнь 8, 2024
Abstract
Evapotranspiration
plays
a
pivotal
role
in
the
hydrological
cycle.
It
is
essential
to
develop
an
accurate
computational
model
for
predicting
reference
evapotranspiration
(RET)
agricultural
and
applications,
especially
management
of
irrigation
systems,
allocation
water
resources,
assessments
utilization
demand
use
allocations
rural
urban
areas.
The
limitation
climatic
data
estimate
RET
restricted
standard
Penman–Monteith
method
recommended
by
food
agriculture
organization
(FAO-PM56).
Therefore,
current
study
used
such
as
minimum,
maximum
mean
air
temperature
(
T
max
,
min
),
relative
humidity
(RH
wind
speed
U
)
sunshine
hours
N
predict
using
gene
expression
programming
(GEP)
technique.
In
this
study,
total
17
different
input
meteorological
combinations
were
models.
obtained
results
each
GEP
are
compared
with
FAO-PM56
evaluate
its
performance
both
training
testing
periods.
GEP-13
RH
showed
lowest
errors
(RMSE,
MAE)
highest
efficiencies
R
2
NSE)
semi-arid
(Faisalabad
Peshawar)
humid
(Skardu)
conditions
while
GEP-11
GEP-12
perform
best
arid
(Multan,
Jacobabad)
during
period.
However,
Multan
Jacobabad,
GEP-7
Faisalabad,
GEP-1
Peshawar,
Islamabad
Skardu
outperformed
phase,
models
values
reach
0.99,
RMSE
ranged
from
0.27
2.65,
MAE
0.21
1.85
NSE
0.18
0.99.
findings
indicate
that
effective
when
there
minimal
data.
Additionally,
was
identified
most
relevant
factor
across
all
conditions.
may
be
planning
resources
practical
situations,
they
demonstrate
impact
variables
on
associated
