Research on Water Resource Carrying Capacity Assessment and Water Quality Forecasting Based on Feature Selection with CNN-BiLSTM-Attention Model of the Min River Basin
Water,
Год журнала:
2025,
Номер
17(6), С. 824 - 824
Опубликована: Март 13, 2025
To
achieve
a
more
accurate
assessment
of
water
resource
carrying
capacity
(WRCC),
the
indicators
resources,
social
and
ecological
environment
were
selected
to
construct
WRCC
system
on
basis
combinatorial
assignment
method
with
advantages.
Moreover,
incorporation
key
quality
influences
into
predictions
facilitated
performance
predictive
models.
Adaptive
Lasso
Regression
was
used
select
factors
affecting
quality,
whereas
CatBoost
algorithm
ranked
importance
by
in
prediction
model.
The
Convolutional
Neural
Network-Bidirectional
Long
Short-Term
Memory-Attention
(CNN-BiLSTM-Attention)
model
forecast
WQI.
research
results
propose
new
evaluation
method.
show
that
average
barrier
levels
for
socio-economic
development,
34.97%,
34.93%,
30.10%,
respectively.
Compared
other
layers
WRCC,
obstacle
degree
layer
has
always
been
lower.
total
sewage
treatment,
greening
coverage
built-up
areas,
per
capita
green
space
parks
main
within
Min
River
Basin.
Based
factor
screening,
it
can
be
seen
dissolved
oxygen
is
positively
correlated
watershed,
while
influencing
are
negatively
Total
nitrogen
had
greatest
impact
conditions
regression
coefficient
−1.7532.
From
comparison
results,
known
hybrid
make
MAE
value
45%
monitoring
points
reach
minimum,
RMSE
35%
minimum.
percentages
remaining
models
reached
lowest
values
15%
20%
30%,
models,
MSE
relatively
small,
which
conducive
predicting
Язык: Английский
Modelling the impact of climate change on runoff and sediment yield in Mediterranean basins: the Carapelle case study (Apulia, Italy)
Frontiers in Water,
Год журнала:
2025,
Номер
7
Опубликована: Март 13, 2025
Introduction
This
study
analyzes
the
impact
of
climate
change
on
streamflow
and
sediment
yield
in
Carapelle
basin,
a
Mediterranean
watershed
located
Apulia
Region
Italy.
Methods
Three
model
projections
(CMCC,
MPI,
EC-EARTH)
under
CMIP6
SSP2-4.5
scenario
were
bias-corrected
evaluated
using
statistical
measures
to
ensure
enhanced
fit
with
observed
data.
The
Soil
Water
Assessment
Tool
(SWAT)
was
implemented
simulate
hydrology
yield.
calibrated
validated
measured
load
data
from
2004–2011,
demonstrating
satisfactory
performance
for
both
parameters.
Baseline
conditions
(2000–2020)
compared
future
(2030–2050).
Results
Climate
2030-2050
indicated
temperature
increases
up
1.3°C
average
annual
rainfall
decreases
38%
baseline.
These
changes
resulted
reduced
water
across
all
models.
CMCC
projected
highest
reduction
mean
flow
(67%),
smaller
reductions
MPI
(35%)
EC-EARTH
(7%).
Correspondingly,
52.8%
(CMCC),
41.7%
(MPI),
18.1%
(EC-EARTH).
Despite
these
overall
reductions,
spatial
analysis
revealed
that
soil
erosion
remained
critical
(sediment
>10
t
ha
−1
)
certain
areas,
particularly
steep
slopes
wheat
cultivation.
Discussion
Integrating
considerations
into
management
strategies
is
essential
sustaining
river
basins
conditions.
Adaptation
such
as
BMPs
NBSs
should
be
reduce
mitigate
impacts.
Язык: Английский