Dynamic classification and attention mechanism-based bidirectional long short-term memory network for daily runoff prediction in Aksu River basin, Northwest China
Journal of Environmental Management,
Год журнала:
2025,
Номер
374, С. 124121 - 124121
Опубликована: Янв. 15, 2025
Язык: Английский
Advanced Temporal Deep Learning Framework for Enhanced Predictive Modeling in Industrial Treatment Systems
S Ramya,
S Srinath,
Pushpa Tuppad
и другие.
Results in Engineering,
Год журнала:
2025,
Номер
unknown, С. 104158 - 104158
Опубликована: Янв. 1, 2025
Язык: Английский
Small hydropower impacts on water quality: a comparative analysis of different assessment methods
Water Resources and Industry,
Год журнала:
2025,
Номер
unknown, С. 100282 - 100282
Опубликована: Фев. 1, 2025
Язык: Английский
Developing a real-time water quality simulation toolbox using machine learning and application programming interface
Journal of Environmental Management,
Год журнала:
2025,
Номер
377, С. 124719 - 124719
Опубликована: Фев. 28, 2025
Язык: Английский
The role of optimizers in developing data-driven model for predicting lake water quality incorporating advanced water quality model
Alexandria Engineering Journal,
Год журнала:
2025,
Номер
122, С. 411 - 435
Опубликована: Март 18, 2025
Язык: Английский
Simulating the Deterioration Behavior of Tunnel Elements Using Amalgamation of Regression Trees and State-of-the-Art Metaheuristics
Mathematics,
Год журнала:
2025,
Номер
13(7), С. 1021 - 1021
Опубликована: Март 21, 2025
Tunnel
infrastructures
worldwide
face
escalating
deterioration
challenges
due
to
aging
materials,
increasing
load
demands,
and
exposure
harsh
environmental
conditions.
Accurately
predicting
the
onset
progression
of
is
paramount
for
ensuring
structural
safety,
optimizing
maintenance
interventions,
prolonging
service
life.
However,
complex
interplay
environmental,
material,
operational
factors
poses
significant
current
predictive
models.
Additionally,
they
are
constrained
by
small
datasets
a
narrow
range
tunnel
elements
that
limit
their
generalizability.
This
paper
presents
novel
hybrid
metaheuristic-based
regression
tree
(REGT)
model
designed
enhance
accuracy
robustness
predictions.
Leveraging
metaheuristic
algorithms’
strengths,
developed
method
jointly
optimizes
critical
hyperparameters
identifies
most
relevant
features
prediction.
A
comprehensive
dataset
encompassing
material
properties,
stressors,
traffic
loads,
historical
condition
assessments
was
compiled
development.
Comparative
analyses
against
conventional
trees,
artificial
neural
networks,
support
vector
machines
demonstrated
consistently
outperformed
baseline
techniques
regarding
While
trees
classic
machine
learning
models,
no
single
variant
dominated
all
elements.
Furthermore,
optimization
framework
mitigated
overfitting
provided
interpretable
insights
into
primary
driving
deterioration.
Finally,
findings
this
research
highlight
potential
models
as
powerful
tools
infrastructure
management,
offering
actionable
predictions
enable
proactive
strategies
resource
optimization.
study
contributes
advancing
field
modeling
in
civil
engineering,
with
implications
sustainable
management
infrastructure.
Язык: Английский
An improved graph neural network integrating indicator attention and spatio-temporal correlation for dissolved oxygen prediction
Ecological Informatics,
Год журнала:
2025,
Номер
unknown, С. 103126 - 103126
Опубликована: Апрель 1, 2025
Язык: Английский
A comprehensive review of various environmental factors' roles in remote sensing techniques for assessing surface water quality
The Science of The Total Environment,
Год журнала:
2024,
Номер
957, С. 177180 - 177180
Опубликована: Ноя. 23, 2024
Язык: Английский
Decontamination of fish aquarium wastewater by ozonation catalyzed by multi-metal loaded activated carbons for sustainable aquaculture
Process Safety and Environmental Protection,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 1, 2024
Язык: Английский
Machine Learning-Enhanced Water and Air Quality Monitoring Technologies and Applications
E Varun,
L. Rajesh,
M. Lokeshwari
и другие.
Advances in IT standards and standardization research (AISSR) book series/Advances in IT standards and standardization research series,
Год журнала:
2024,
Номер
unknown, С. 79 - 110
Опубликована: Дек. 18, 2024
This
chapter
points
out
machine
learning-Ml-that
is
set
to
alter
air
and
water
quality
monitoring
technologies.
Traditional
systems
usually
work
satisfactorily;
however,
there
always
exists
deficiencies
regarding
data
processing
accuracy
of
response
in
real
time.
For
this
kind
system,
ML
algorithms
would
become
advantageous
for
such
carry
large-scale
environmental
analysis,
thereby
enhancing
predictive
capabilities
realize
early
detection
pollutants.
The
goes
on
explain
techniques
under
supervised
unsupervised
learning,
along
with
their
applications
sensor
networks,
remote
sensing,
fusion.
Case
studies
the
successful
implementation
ML-driven
solution
show
improvement
decision-making.
Further,
addresses
challenge
associated
integration
privacy,
algorithmic
bias,
need
robust
training
datasets.
Язык: Английский