Electronics,
Год журнала:
2023,
Номер
12(4), С. 1007 - 1007
Опубликована: Фев. 17, 2023
The
techniques
for
forecasting
meteorological
variables
are
highly
studied
since
prior
knowledge
of
them
allows
the
efficient
management
renewable
energies,
and
also
other
applications
science
such
as
agriculture,
health,
engineering,
energy,
etc.
In
this
research,
design,
implementation,
comparison
models
have
been
performed
using
different
Machine
Learning
part
Python
open-source
software.
implemented
include
multiple
linear
regression,
polynomial
random
forest,
decision
tree,
XGBoost,
multilayer
perceptron
neural
network
(MLP).
To
identify
best
technique,
mean
square
error
(RMSE),
absolute
percentage
(MAPE),
(MAE),
coefficient
determination
(R2)
used
evaluation
metrics.
most
depend
on
variable
to
be
forecasting,
however,
it
is
noted
that
them,
forest
XGBoost
present
better
performance.
For
temperature,
performing
technique
was
Random
Forest
with
an
R2
0.8631,
MAE
0.4728
°C,
MAPE
2.73%,
RMSE
0.6621
°C;
relative
humidity,
0.8583,
2.1380RH,
2.50%
2.9003
RH;
solar
radiation,
0.7333,
65.8105
W/m2,
105.9141
W/m2;
wind
speed,
0.3660,
0.1097
m/s,
0.2136
m/s.
Artificial Intelligence in Agriculture,
Год журнала:
2022,
Номер
6, С. 211 - 229
Опубликована: Янв. 1, 2022
The
agriculture
industry
is
undergoing
a
rapid
digital
transformation
and
growing
powerful
by
the
pillars
of
cutting-edge
approaches
like
artificial
intelligence
allied
technologies.
At
core
intelligence,
deep
learning-based
computer
vision
enables
various
activities
to
be
performed
automatically
with
utmost
precision
enabling
smart
into
reality.
Computer
techniques,
in
conjunction
high-quality
image
acquisition
using
remote
cameras,
enable
non-contact
efficient
technology-driven
solutions
agriculture.
This
review
contributes
providing
state-of-the-art
technologies
based
on
learning
that
can
assist
farmers
operations
starting
from
land
preparation
harvesting
operations.
Recent
works
area
were
analyzed
this
paper
categorized
(a)
seed
quality
analysis,
(b)
soil
(c)
irrigation
water
management,
(d)
plant
health
(e)
weed
management
(f)
livestock
(g)
yield
estimation.
also
discusses
recent
trends
such
as
generative
adversarial
networks
(GAN),
transformers
(ViT)
other
popular
architectures.
Additionally,
study
pinpoints
challenges
implementing
farmer’s
field
real-time.
overall
finding
indicates
convolutional
neural
are
corner
stone
modern
their
architectures
provide
across
terms
accuracy.
However,
success
approach
lies
building
model
dataset
real-time
solutions.
Land,
Год журнала:
2022,
Номер
11(11), С. 2040 - 2040
Опубликована: Ноя. 14, 2022
Climate
change
has
caused
droughts
to
increase
in
frequency
and
severity
worldwide,
which
attracted
scientists
create
drought
prediction
models
mitigate
the
impacts
of
droughts.
One
most
important
challenges
addressing
is
developing
accurate
predict
their
discrete
characteristics,
i.e.,
occurrence,
duration,
severity.
The
current
research
examined
performance
several
different
machine
learning
models,
including
Artificial
Neural
Network
(ANN)
M5P
Tree
forecasting
widely
used
measure,
Standardized
Precipitation
Index
(SPI),
at
both
time
scales
(SPI
3,
SPI
6).
model
was
developed
utilizing
rainfall
data
from
two
stations
India
(i.e.,
Angangaon
Dahalewadi)
for
2000–2019,
wherein
first
14
years
are
employed
training,
while
remaining
six
validation.
subset
regression
analysis
performed
on
12
input
combinations
choose
best
combination
3
6.
sensitivity
carried
out
given
find
effective
parameter
forecasting.
all
ANN
(4,
5),
(5,
6),
(6,
7),
assessed
through
statistical
indicators,
namely,
MAE,
RMSE,
RAE,
RRSE,
r.
results
revealed
that
(t-1)
sensitive
parameters
with
highest
values
β
=
0.916,
1.017,
respectively,
SPI-3
SPI-6
7
(SPI-1/SPI-3/SPI-4/SPI-5/SPI-8/SPI-9/SPI-11)
4
(SPI-1/SPI-2/SPI-6/SPI-7)
based
higher
R2
Adjusted
lowest
MSE
values.
It
clear
r
lesser
RMSE
as
compared
7)
models.
Therefore,
superior
other
stations.
Sustainability,
Год журнала:
2023,
Номер
15(2), С. 1109 - 1109
Опубликована: Янв. 6, 2023
The
prediction
of
hydrological
droughts
is
vital
for
surface
and
ground
waters,
reservoir
levels,
hydroelectric
power
generation,
agricultural
production,
forest
fires,
climate
change,
the
survival
living
things.
This
study
aimed
to
forecast
1-month
lead-time
in
Yesilirmak
basin.
For
this
purpose,
support
vector
regression,
Gaussian
process
regression
tree,
ensemble
tree
models
were
used
alone
combination
with
a
discrete
wavelet
transform.
Streamflow
drought
index
values
determine
droughts.
data
divided
into
70%
training
(1969–1998)
30%
(1999–2011)
testing.
performance
was
evaluated
according
various
statistical
criteria
such
as
mean
square
error,
root
means
absolute
determination
coefficient.
As
result,
it
determined
that
obtained
by
decomposing
subcomponents
transform
optimal.
In
addition,
most
effective
drought-predicting
model
using
db10
MGPR
algorithm
squared
error
0.007,
0.08,
0.04,
coefficient
(R2)
0.99
at
station
1413.
weakest
stand-alone
FGSV
(RMSE
0.88,
RMSE
0.94,
MAE
0.76,
R2
0.14).
Moreover,
revealed
main
more
accurate
predicting
short-term
than
other
wavelets.
These
results
provide
essential
information
decision-makers
planners
manage
Civil Engineering Journal,
Год журнала:
2023,
Номер
9(11), С. 2630 - 2648
Опубликована: Ноя. 1, 2023
The
Ardabil
Plain
is
pivotal
in
the
national
agricultural
sector
and
ranks
among
leading
horticultural
production
provinces.
primary
objective
of
this
study
to
enhance
environmental
sustainability
critical
vulnerable
region,
particularly
face
imminent
droughts
climate
change.
examines
impacts
change
on
agriculture
tourism
area.
It
puts
forward
suggestions
for
implementing
sustainable
practices
safeguard
well-being
local
population.
results
indicate
a
38%
reduction
precipitation,
especially
autumn
season,
with
possible
alteration
timing
strength
rainfall.
Also,
notable
decline
volume,
specific
region
plain,
has
been
observed.
currently
produces
284,182
tons
wheat,
204,980
from
irrigated
crops
79,202
rain-fed
crops.
However,
projected
future
scenario
indicates
decrease
total
wheat
209,196
tons,
160,125
49,071
This
expected
lead
net
income
loss
approximately
-$75,389,059,
-$45,095,663
attributed
-$30,293,396
findings
suggest
that
availability
water
sources
certain
regions
may
prompt
shift
farming
land
north
south
plain
promote
sustainability.
demographic
could
have
significant
financial
social
implications
region's
growth
prosperity.
Moreover,
increasing
temperatures
western
northern
pose
flood
risks
uncomfortable
travel
conditions,
concerning
given
reliance
potential
unemployment
consequences.
becomes
imperative
adopt
manage
resources
effectively
ensure
resilience
prosperity
challenges.
Doi:
10.28991/CEJ-2023-09-11-01
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