Contribution and behavioral assessment of physical and anthropogenic factors for soil erosion using integrated deep learning and game theory
Journal of Cleaner Production,
Journal Year:
2023,
Volume and Issue:
416, P. 137689 - 137689
Published: June 26, 2023
Language: Английский
Quantifying soil erosion and influential factors in Guwahati's urban watershed using statistical analysis, machine and deep learning
Remote Sensing Applications Society and Environment,
Journal Year:
2023,
Volume and Issue:
33, P. 101088 - 101088
Published: Nov. 10, 2023
Language: Английский
An artificial intelligence-based assessment of soil erosion probability indices and contributing factors in the Abha-Khamis watershed, Saudi Arabia
Frontiers in Ecology and Evolution,
Journal Year:
2023,
Volume and Issue:
11
Published: June 6, 2023
Soil
erosion
is
a
major
problem
in
arid
regions,
including
the
Abha-Khamis
watershed
Saudi
Arabia.
This
research
aimed
to
identify
soil
erosional
probability
using
various
erodibility
indices,
clay
ratio
(CR),
modified
(MCR),
Critical
Level
of
Organic
Matter
(CLOM),
and
principle
component
analysis
based
index
(SEI).
To
achieve
these
objectives,
study
used
t
-tests
an
artificial
neural
network
(ANN)
model
best
SEI
for
management.
The
performance
models
were
then
evaluated
R
2
,
Root
Mean
Squared
Error
(RMSE),
(MSE),
Absolute
(MAE),
with
CLOM
identified
as
predicting
erodibility.
Additionally,
Shapley
additive
explanations
(SHAP)
values
influential
parameters
erosion,
sand,
clay,
silt,
organic
carbon
(SOC),
moisture,
void
ratio.
information
can
help
develop
management
strategies
oriented
parameters,
which
will
prevent
erosion.
showed
notable
distinctions
between
CR
CLOM,
where
25–27%
contribution
explained
over
89%
overall
diversity.
MCR
indicated
that
70%
area
had
low
erodibility,
while
20%
moderate
10%
high
range
from
40%
showing
moderate,
high.
Based
on
T
-test
results,
significantly
different
MCR,
principal
(PCA),
PCA,
PCA.
ANN
implementation
demonstrated
highest
accuracy
(
0.95
training
0.92
testing)
SOC,
being
most
important
variables.
SHAP
confirmed
importance
variables
each
four
models.
provides
valuable
regions.
identification
effective
promote
agricultural
production.
be
by
policymakers
stakeholders
make
informed
decisions
manage
Language: Английский
Novel hybrid ravine vulnerability index‐based identification of potential reclamation zones for Western India
Land Degradation and Development,
Journal Year:
2023,
Volume and Issue:
35(2), P. 849 - 866
Published: Oct. 24, 2023
Abstract
The
ravine
is
often
regarded
as
the
worst
instance
of
how
water
erosion
causing
land
deterioration.
farmers'
livelihoods
are
threatened
by
ravine's
expansion
into
surrounding
50
m
buffer
zone,
leaving
them
with
no
choice
except
to
work
landless
workers.
Due
high
expense
restoration,
it
not
economically
possible
begin
process
reclamation
in
all
lands
at
once.
As
a
result,
necessary
recognize
prospective
regions
bring
activities.
In
this
work,
vulnerability
index
for
Mahi
Western
India
was
developed
using
cutting‐edge
hybrid
methodology.
For
development
index,
determined
that
sand,
silt,
clay,
organic
carbon,
soil
erodibility
factor,
slope,
stream
power
topographic
wetness
sediment
transport
and
cover
factor
were
crucial
components.
Weights
applied
various
parameters
based
on
perceived
significance
each
parameter
relation
another
decision
matrix
analytical
hierarchical
process.
number
iterations
made
reach
consistency
ratio
under
10%
determine
final
priority
weights
parameter.
had
lowest
weight
(1.4),
whereas
sand
highest
(28.5).
purposes,
active
zones
inside
designated
50‐m
which
covered
an
area
63,031
acres,
determined.
According
ground
truth‐validated
found
16,703
ha
(26.50%)
region
ravine,
given
extremely
priority.
per
capacity
classification,
20,275
(32.16%)
arable
land,
18,687
(29.65%)
non‐arable
ideal
conservation
treatments.
Language: Английский