A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management
Applied Sciences,
Год журнала:
2023,
Номер
13(22), С. 12147 - 12147
Опубликована: Ноя. 8, 2023
This
paper
offers
a
comprehensive
overview
of
machine
learning
(ML)
methodologies
and
algorithms,
highlighting
their
practical
applications
in
the
critical
domain
water
resource
management.
Environmental
issues,
such
as
climate
change
ecosystem
destruction,
pose
significant
threats
to
humanity
planet.
Addressing
these
challenges
necessitates
sustainable
management
increased
efficiency.
Artificial
intelligence
(AI)
ML
technologies
present
promising
solutions
this
regard.
By
harnessing
AI
ML,
we
can
collect
analyze
vast
amounts
data
from
diverse
sources,
remote
sensing,
smart
sensors,
social
media.
enables
real-time
monitoring
decision
making
applications,
including
irrigation
optimization,
quality
monitoring,
flood
forecasting,
demand
enhance
agricultural
practices,
distribution
models,
desalination
plants.
Furthermore,
facilitates
integration,
supports
decision-making
processes,
enhances
overall
sustainability.
However,
wider
adoption
faces
challenges,
heterogeneity,
stakeholder
education,
high
costs.
To
provide
an
management,
research
focuses
on
core
fundamentals,
major
(prediction,
clustering,
reinforcement
learning),
ongoing
issues
offer
new
insights.
More
specifically,
after
in-depth
illustration
algorithmic
taxonomy,
comparative
mapping
all
specific
tasks.
At
same
time,
include
tabulation
works
along
with
some
concrete,
yet
compact,
descriptions
objectives
at
hand.
leveraging
tools,
develop
plans
address
world’s
supply
concerns
effectively.
Язык: Английский
Sustainable groundwater management in coastal cities: Insights from groundwater potential and vulnerability using ensemble learning and knowledge-driven models
Journal of Cleaner Production,
Год журнала:
2024,
Номер
442, С. 141152 - 141152
Опубликована: Фев. 1, 2024
Язык: Английский
Analysis of machine learning models and data sources to forecast burst pressure of petroleum corroded pipelines: A comprehensive review
Engineering Failure Analysis,
Год журнала:
2023,
Номер
155, С. 107747 - 107747
Опубликована: Ноя. 3, 2023
Язык: Английский
Data augmentation using SMOTE technique: Application for prediction of burst pressure of hydrocarbons pipeline using supervised machine learning models
Results in Engineering,
Год журнала:
2024,
Номер
unknown, С. 103233 - 103233
Опубликована: Окт. 1, 2024
Язык: Английский
A New Approach Based on Deep Neural Networks and Multisource Geospatial Data for Spatial Prediction of Groundwater Spring Potential
IEEE Access,
Год журнала:
2024,
Номер
12, С. 26344 - 26363
Опубликована: Янв. 1, 2024
Groundwater
spring
plays
a
crucial
role
in
human
life,
including
water
resource
management
and
planning;
therefore,
developing
accurate
prediction
models
for
groundwater
potential
mapping
is
essential.
The
objective
of
this
research
to
introduce
confirm
new
modeling
approach
based
on
TensorFlow
Deep
Neural
Networks
(TF-DNN)
multisource
geospatial
data
spatial
potential,
with
case
study
the
tropical
province
central
highland
Vietnam.
For
task,
TF-DNN
model
structure
three
hidden
layers
32
neurons
each
was
established;
therein,
Adaptive
Moment
Estimation
(ADAM)
algorithm
used
as
an
optimizer,
whereas
Rectified
Linear
Unit
(ReLU)
activation
function,
sigmoid
transfer
function.
A
database
area,
consisting
733
locations
12
influencing
factors,
prepared
ArcGIS
Pro.
Then,
it
develop
verify
model.
Decision
Tree,
Support
Vector
Machine,
Logistic
Regression,
Random
Forest,
Classification
Regression
Trees
were
benchmark
comparison.
results
demonstrate
that
proposed
(Accuracy
=
80.5%,
F-score
0.797,
AUC
0.864)
achieves
high
global
performance,
outperforming
models.
Thus,
represents
novel
effective
tool
spatially
predicting
mapping.
map
generated
has
assist
provincial
authorities
formulating
strategies
concerning
socio-economic
development.
Язык: Английский
A self-organizing map-based approach for groundwater model parameter identification
Stochastic Environmental Research and Risk Assessment,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 28, 2025
Язык: Английский
A high-precision and interpretability-enhanced direct inversion framework for groundwater contaminant source identification using multiple machine learning techniques
Journal of Hydrology,
Год журнала:
2025,
Номер
unknown, С. 133237 - 133237
Опубликована: Апрель 1, 2025
Язык: Английский
Architectural Design of Electricity Power Consumption Misuse Detection Based on Light Gradient Boosting Machine Using Blockchain Technology
Опубликована: Дек. 20, 2023
This
research
focuses
on
blockchain
architecture
design
for
Electricity
misuse
detection
is
a
critical
concern
in
contemporary
energy
management
systems.
Detecting
and
preventing
unauthorized
or
improper
usage
poses
significant
challenge
due
to
the
complex
diverse
nature
of
consumption
data.
improving
accuracy
electricity
through
utilization
LightGBM
classification
model
machine
learning.
Light
Gradient
Boosting
Machine
(LightGBM)
methods
are
employed
their
efficiency,
accuracy,
ability
handle
imbalanced
datasets.
Evaluation
metrics,
such
as
AUC
(Area
Under
Curve),
used
assess
performance
model.
The
results
demonstrate
that
classifier
exhibits
superior
accurately
detecting
instances
misuse.
It
achieves
an
impressive
84.19
%
indicating
its
effectiveness
identifying
flagging
events.
By
leveraging
LightBGM
algorithms,
valuable
insights
can
be
obtained
decision-making
processes,
aiding
improvement
strategies
overall
performance.
analysis
also
identifies
key
factors
influencing
Block
chain
assist
both
providers
policymakers
developing
effective
measures
curb
decentralized
manner
so
security
guaranteed.
Язык: Английский