Soil and Sediment Contamination An International Journal,
Год журнала:
2024,
Номер
unknown, С. 1 - 19
Опубликована: Янв. 21, 2024
The
problem
of
soil
heavy
metal
pollution
in
decommissioned
sites
has
become
an
environmental
threat
and
challenge
faced
by
countries
around
the
world.
Establishing
a
high-precision
3D
model
contaminants
is
essential
for
risk
assessment
accurate
monitoring
contaminated
sites.
In
this
study,
geological
SSA-XGBoost
are
proposed
to
predict
concentration
site.
These
models
can
effectively
improve
prediction
accuracy
metals,
RMSE
XGBoost
optimized
SSA
algorithm
reduced
24.3%-34.3%.
Compared
with
other
machine
learning
models,
optimal
performance
improving
metals.
It
suitable
areas
significant
spatial
heterogeneity
Using
model,
distribution
characteristics
metals
determined.
pollutants
ranked
as
As>Pb>Mo,
overall
degree
decreases
gradually
from
top
bottom.
mainly
distributed
production
workshop
area
southwest
site,
miscellaneous
fill
layer
main
that
needs
be
remediated.
Concurrency and Computation Practice and Experience,
Год журнала:
2024,
Номер
36(17)
Опубликована: Апрель 24, 2024
Summary
This
study
underscores
the
growing
significance
of
multimodal
transportation
within
cargo
sector
and
its
consequential
environmental
impacts.
We
present
a
novel
mathematical
model
for
operation
scheduling,
incorporating
variables
such
as
resource
availability,
customer
service
benchmarks,
considerations.
Our
objective
is
to
mitigate
expenses
reduce
delivery
delays.
The
proposed
approach
advocates
LU
decomposition
with
pivot
strategy
rapid
resolution,
adherence
convergence
criteria,
optimization
cost
strategies,
efficient
utilization.
Leveraging
adaptive
neural
fuzzy
inference
system
(ANFIS)
genetic
algorithm
(GA),
our
methodology
facilitates
learning
from
past
decisions
enhance
solutions,
aligning
supply,
demand
efficiently.
evaluate
financial
implications
across
four
scenarios,
offering
insights
into
economic
advantages
various
modes—trains,
ships,
airplanes—compared
truck
transportation,
specific
focus
on
CO
2
emission
Implementing
ANFIS+GA
in
scenarios
yield
impressive
results:
minimal
MAPE
0.17%,
R
0.996,
emissions
0.13%,
0.996.
By
identifying
cost‐efficient
routes
optimizing
allocations,
enables
informed
regarding
vehicle
distribution,
supplier
selection,
contract
negotiations.
Additionally,
we
use
establish
risk
threshold,
crucial
comparing
trade
variances.
Multimodal
typically
lower
emissions,
favoring
buying
allowances
low
selling
them
high.
Notably,
threshold
affects
low‐emission
provider
utilization,
impacting
emissions.
With
0.12
an
price
1.2,
ANFIS+GA‐based
achieves
significant
−20%
deviation
PLoS ONE,
Год журнала:
2024,
Номер
19(6), С. e0300036 - e0300036
Опубликована: Июнь 6, 2024
With
the
continuous
development
of
large-scale
engineering
projects
such
as
construction
projects,
relief
support,
and
relocation
in
various
countries,
logistics
has
attracted
much
attention.
This
paper
addresses
a
multimodal
material
route
planning
problem
(MMRPP),
which
considers
transportation
from
suppliers
to
work
zones
using
multiple
transport
modes.
Due
overall
relevance
technical
complexity
logistics,
we
introduce
key
processes
at
generate
solution,
is
more
realistic
for
real-life
applications.
We
propose
multi-objective
model
that
minimizes
total
cost
time.
The
by
ε
−
constraint
method
transforms
objective
function
minimizing
into
constraint,
resulting
obtaining
pareto
optimal
solutions.
makes
up
lack
existing
research
on
combination
both
transportation,
after
feasibility
algorithm
verified
examples.
results
show
solution
with
introduction
produces
time-efficient
less
time-consuming
results,
obtained
are
reliable
than
traditional
methods
solving
problems
line
decision
maker’s
needs.
Soil and Sediment Contamination An International Journal,
Год журнала:
2024,
Номер
unknown, С. 1 - 19
Опубликована: Янв. 21, 2024
The
problem
of
soil
heavy
metal
pollution
in
decommissioned
sites
has
become
an
environmental
threat
and
challenge
faced
by
countries
around
the
world.
Establishing
a
high-precision
3D
model
contaminants
is
essential
for
risk
assessment
accurate
monitoring
contaminated
sites.
In
this
study,
geological
SSA-XGBoost
are
proposed
to
predict
concentration
site.
These
models
can
effectively
improve
prediction
accuracy
metals,
RMSE
XGBoost
optimized
SSA
algorithm
reduced
24.3%-34.3%.
Compared
with
other
machine
learning
models,
optimal
performance
improving
metals.
It
suitable
areas
significant
spatial
heterogeneity
Using
model,
distribution
characteristics
metals
determined.
pollutants
ranked
as
As>Pb>Mo,
overall
degree
decreases
gradually
from
top
bottom.
mainly
distributed
production
workshop
area
southwest
site,
miscellaneous
fill
layer
main
that
needs
be
remediated.
