Journal of Southwest Jiaotong University,
Год журнала:
2024,
Номер
59(1)
Опубликована: Янв. 1, 2024
The
sudden
vaname
shrimp
death
factors
are
overfeeding,
disease
infection,
failure
to
mount,
stress,
and
high
rainfall,
which
cause
potential
changes
in
water
pH
that
trigger
anxiety
shrimp.
next
factor
is
turbid
or
dirty
water.
Other
include
insufficient
oxygen
content
the
Therefore,
overcome
these
issues,
a
sensor
explicitly
handles
conditions
needed.
system
built
this
research
still
has
two
stages,
namely
real-time
monitoring
automatic
actuators,
being
developed.
placed
floating
condition
with
specific
materials
so
it
impossible
sink.
Some
tested
sensors
salinity,
pH,
turbidity,
dissolved
sensors.
Its
wireless
telecommunication
uses
LoRa
frequencies
of
920–923
MHz.
It
an
8-dBi
omnidirectional
antenna
Dragino
RFM96
Module
chip.
This
provides
data
on
entire
environment
needed
for
survive.
development
after
focused
actuator,
how
turn
Blower
automatically
needs
results
shown
from
experiment
all
brackish
quality
measurements
running
normally;
displayed
application
server
real
time
using
Tago.io
app
been
connected
LoRaWAN
Module,
915
MHz
found
end
devices.
installed
were
calibrated
produce
accurate
data.
Keywords:
environment,
innovative
sensor,
farming,
monitoring,
smart
aquaculture
DOI:
https://doi.org/10.35741/issn.0258-2724.59.1.18
Water,
Год журнала:
2024,
Номер
16(5), С. 707 - 707
Опубликована: Фев. 28, 2024
Dissolved
oxygen
(DO)
concentration
is
a
pivotal
determinant
of
water
quality
in
freshwater
lake
ecosystems.
However,
rapid
population
growth
and
discharge
polluted
wastewater,
urban
stormwater
runoff,
agricultural
non-point
source
pollution
runoff
have
triggered
significant
decline
DO
levels
Lake
Erie
other
lakes
located
populated
temperate
regions
the
globe.
Over
eleven
million
people
rely
on
Erie,
which
has
been
adversely
impacted
by
anthropogenic
stressors
resulting
deficient
concentrations
near
bottom
Erie’s
Central
Basin
for
extended
periods.
In
past,
hybrid
long
short-term
memory
(LSTM)
models
successfully
used
time-series
forecasting
rivers
ponds.
prediction
errors
tend
to
grow
significantly
with
period.
Therefore,
this
research
aimed
improve
accuracy
taking
advantage
real-time
(water
temperature
concentration)
monitoring
network
establish
temporal
spatial
links
between
adjacent
stations.
We
developed
LSTM
that
combine
LSTM,
convolutional
neuron
(CNN-LSTM),
CNN
gated
recurrent
unit
(CNN-GRU)
models,
(ConvLSTM)
forecast
near-bottom
Basin.
These
their
capacity
handle
complicated
datasets
variability.
can
serve
as
accurate
reliable
tools
help
environmental
protection
agencies
better
access
manage
health
these
vital
Following
analysis
21-site
dataset
2020
2021,
ConvLSTM
model
emerged
most
reliable,
boasting
an
MSE
0.51
mg/L,
MAE
0.42
R-squared
0.95
over
12
h
range.
The
foresees
future
hypoxia
Erie.
Notably,
site
713
holds
significance
indicated
outcomes
derived
from
Shapley
additive
explanations
(SHAP).
PLoS ONE,
Год журнала:
2025,
Номер
20(4), С. e0321637 - e0321637
Опубликована: Апрель 23, 2025
With
China’s
rapid
urbanization
and
the
increasing
frequency
of
extreme
weather
events,
heavy
rainfall-induced
urban
waterlogging
has
become
a
persistent
pressing
challenge.
Accurately
predicting
depth
is
essential
for
disaster
prevention
loss
mitigation.
However,
existing
hydrological
models
often
require
extensive
data
have
complex
structures,
resulting
in
low
prediction
accuracy
limited
generalization
capabilities.
To
address
these
challenges,
this
paper
proposes
hybrid
deep
learning-based
approach,
BiTCN-GRU
model,
flood-prone
areas.
This
model
integrates
Bidirectional
Temporal
Convolutional
Networks
(BiTCN)
Gated
Recurrent
Units
(GRU)
to
enhance
performance.
Specifically,
gated
recurrent
units
employed
task.
temporal
convolutional
network
can
effectively
capture
information
features
during
rainfall
by
forward
backward
convolution
use
them
as
inputs
GRU.
Experimental
results
demonstrate
great
performance
proposed
achieving
MAE,
RMSE,
R
2
values
1.56,
3.62,
88.31%
Minshan
Road,
3.44,
8.08,
92.64%
Huaihe
Road
datasets,
respectively.
Compared
such
GBDT,
LSTM,
TCN-LSTM,
exhibits
higher
depth.
provides
robust
solution
short-term
prediction,
offering
valuable
scientific
insights
theoretical
support
