Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River
Remote Sensing,
Год журнала:
2025,
Номер
17(1), С. 119 - 119
Опубликована: Янв. 2, 2025
The
continuous
and
effective
monitoring
of
the
water
quality
small
rural
rivers
is
crucial
for
sustainable
development.
In
this
work,
machine
learning
models
were
established
to
predict
a
typical
river
based
on
quantity
measured
data
UAV
hyperspectral
images.
Firstly,
spectral
preprocessed
using
fractional
order
derivation
(FOD),
standard
normal
variate
(SNV),
normalization
(Norm)
enhance
response
characteristics
parameters.
Second,
method
combining
Pearson’s
correlation
coefficient
variance
inflation
factor
(PCC–VIF)
was
utilized
decrease
dimensionality
features
improve
input
data.
Again,
screened
features,
back-propagation
neural
network
(BPNN)
model
optimized
mixture
genetic
algorithm
(GA)
particle
swarm
optimization
(PSO)
as
means
estimating
parameter
concentrations.
To
intuitively
evaluate
performance
hybrid
algorithm,
its
prediction
accuracy
compared
with
that
conventional
algorithms
(Random
Forest,
CatBoost,
XGBoost,
BPNN,
GA–BPNN
PSO–BPNN).
results
show
GA–PSO–BPNN
turbidity
(TUB),
ammonia
nitrogen
(NH3-N),
total
(TN),
phosphorus
(TP)
exhibited
optimal
coefficients
determination
(R2)
0.770,
0.804,
0.754,
0.808,
respectively.
Meanwhile,
also
demonstrated
good
robustness
generalization
ability
from
different
periods.
addition,
we
used
visualize
parameters
in
study
area.
This
work
provides
new
approach
refined
rivers.
Язык: Английский
Optical properties of CDOM and assessing eutrophication by remote sensing of CDOM in the Zhanjiang Bay, China
Frontiers in Marine Science,
Год журнала:
2025,
Номер
12
Опубликована: Май 23, 2025
Based
on
the
field
survey
data
collected
in
winter
(January,
2018)
and
spring
(April,
2017),
characteristic
variability
of
chromophoric
dissolved
organic
matter
(CDOM)
different
seasons
Zhanjiang
Bay
was
analyzed.
The
results
demonstrated
that
CDOM
absorption
coefficient
at
280
nm
(
a
g
(280))
spectral
slope
from
275
to
295
(S
275-295
)
representing
molecular
weight
could
both
maintain
good
correlations
with
salinity
spring,
indicating
more
likely
exist
as
conserved
substance
during
its
migration
Bay.
characteristics
weak
correlation
between
Chlorophyll
(Chl
a)
revealed
influence
algal
activity
limited.
In
addition,
this
study
also
suggested
idea
using
track
eutrophication
bay.
acceptable
(280)
reflectance
band
ratio
(R
rs
(704)/R
(492))
recorded
in-situ
,
index
(EI),
series
empirical
models
were
developed
categorize
retrieve
through
then
used
Sentinel-2.
assessment
examined
by
remote
sensing.
This
provided
fresh
approach
measuring
help
regional
environmental
quality
management
organizations
make
informed
decisions.
Язык: Английский
Smart Monitoring Method for Land-Based Sources of Marine Outfalls Based on an Improved YOLOv8 Model
Water,
Год журнала:
2024,
Номер
16(22), С. 3285 - 3285
Опубликована: Ноя. 15, 2024
Land-based
sources
of
marine
outfalls
are
a
major
source
pollution.
The
monitoring
land-based
is
an
important
means
for
environmental
protection
and
governance.
Traditional
on-site
manual
methods
inefficient,
expensive,
constrained
by
geographic
conditions.
Satellite
remote
sensing
spectral
analysis
can
only
identify
pollutant
plumes
affected
discharge
timing
cloud/fog
interference.
Therefore,
we
propose
smart
method
based
on
improved
YOLOv8
model,
using
unmanned
aerial
vehicles
(UAVs).
This
accurately
classify
outfalls,
offering
high
practical
application
value.
Inspired
the
sparse
sampling
in
compressed
sensing,
incorporated
multi-scale
dilated
attention
mechanism
into
model
integrated
dynamic
snake
convolutions
C2f
module.
approach
enhanced
model’s
detection
capability
occluded
complex-feature
targets
while
constraining
increase
computational
load.
Additionally,
proposed
new
loss
calculation
combining
Inner-IoU
(Intersection
over
Union)
MPDIoU
(IoU
with
Minimum
Points
Distance),
which
further
regression
speed
its
ability
to
predict
targets.
final
experimental
results
show
that
achieved
mAP50
(mean
Average
Precision
at
50)
87.0%,
representing
3.4%
from
original
effectively
enabling
outlets.
Язык: Английский