Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Feb. 6, 2025
Improper
management
of
soil
resources
leads
to
the
destruction
organic
carbon
(SOC)
stock
and,
as
a
result,
reduction
quality,
well
accelerating
process
climate
change
through
release
SOC
into
atmosphere.
This
study
was
conducted
evaluate
potential
different
simulation
models
map
spatial
variability
affected
by
land
use
in
area
Qarasu
watershed
Kermanshah
province,
west
Iran.
Map
sampling
points
prepared
using
Latin
hypercube
method.
A
total
168
observation
were
selected
and
profile
dug
described
these
points.
The
samples
taken
horizon
determine
content
laboratory.
mapped
kriging
geostatistical
method
area.
changes
simulated
multivariate
analysis
machine
learning
methods
including
generalized
linear
model
(GLM),
additive
(LAM),
cubist,
random
forest
(RF),
support
vector
(SVM)
models.
Comprehensive
measurement
data
is
utilized
develop
validate
predictive
Predictor
variables
included
16
topographic
two
vegetation,
six
parent
material,
four
climatic
variables.
In-depth
statistical
analyses
are
proposed
performance.
results
showed
that
ranged
from
0.19
8.44
percent
uses.
spherical
variogram
with
MAE
=
0.41
best
fits
interpolate
ordinary
LAM
estimated
wider
range
(SOC
0.18–4.82%)
among
model.
However,
RF
(R2
0.64
RMSE
0.58%)
most
accurate
predicting
quantity
comparing
other
It
can
be
used
reliable
predict
similar
semiarid
regions
West
Asia
Among
predictor
variables,
material's
intrinsic
properties
topography
had
greatest
effect
variability.
