Sustainability,
Journal Year:
2025,
Volume and Issue:
17(5), P. 2250 - 2250
Published: March 5, 2025
Hydrology
relates
to
many
complex
challenges
due
climate
variability,
limited
resources,
and
especially,
increased
demands
on
sustainable
management
of
water
soil.
Conventional
approaches
often
cannot
respond
the
integrated
complexity
continuous
change
inherent
in
system;
hence,
researchers
have
explored
advanced
data-driven
solutions.
This
review
paper
revisits
how
artificial
intelligence
(AI)
is
dramatically
changing
most
important
facets
hydrological
research,
including
soil
land
surface
modeling,
streamflow,
groundwater
forecasting,
quality
assessment,
remote
sensing
applications
resources.
In
AI
techniques
could
further
enhance
accuracy
texture
analysis,
moisture
estimation,
erosion
prediction
for
better
management.
Advanced
models
also
be
used
as
a
tool
forecast
streamflow
levels,
therefore
providing
valuable
lead
times
flood
preparedness
resource
planning
transboundary
basins.
quality,
AI-driven
methods
improve
contamination
risk
enable
detection
anomalies,
track
pollutants
assist
treatment
processes
regulatory
practices.
combined
with
open
new
perspectives
monitoring
resources
at
spatial
scale,
from
forecasting
storage
variations.
paper’s
synthesis
emphasizes
AI’s
immense
potential
hydrology;
it
covers
latest
advances
future
prospects
field
ensure
Frontiers in Artificial Intelligence,
Journal Year:
2025,
Volume and Issue:
7
Published: Jan. 7, 2025
This
systematic
review
provides
a
state-of-art
of
Artificial
Intelligence
(AI)
models
such
as
Machine
Learning
(ML)
and
Deep
(DL)
development
its
applications
in
Mexico
diverse
fields.
These
are
recognized
powerful
tools
many
fields
due
to
their
capability
carry
out
several
tasks
forecasting,
image
classification,
recognition,
natural
language
processing,
machine
translation,
etc.
article
aimed
provide
comprehensive
information
on
the
algorithms
applied
Mexico.
A
total
120
original
research
papers
were
included
details
trends
publication,
spatial
location,
institutions,
publishing
issues,
subject
areas,
applied,
performance
metrics
discussed.
Furthermore,
future
directions
opportunities
presented.
15
areas
identified,
where
Social
Sciences
Medicine
main
application
areas.
It
observed
that
Neural
Networks
(ANN)
preferred,
probably
learn
model
non-linear
complex
relationships
addition
other
popular
Random
Forest
(RF)
Support
Vector
Machines
(SVM).
identified
selection
rely
study
objective
data
patterns.
Regarding
accuracy
recall
most
employed.
paper
could
assist
readers
understanding
techniques
used
area
field
country.
Moreover,
significant
knowledge
implementation
national
AI
strategy,
according
country
needs.
Frontiers in Sustainable Cities,
Journal Year:
2025,
Volume and Issue:
6
Published: Jan. 15, 2025
Introduction
Urban
power
load
forecasting
is
essential
for
smart
grid
planning
but
hindered
by
data
imbalance
issues.
Traditional
single-model
approaches
fail
to
address
this
effectively,
while
multi-model
methods
mitigate
splitting
datasets
incur
high
costs
and
risk
losing
shared
distribution
characteristics.
Methods
A
lightweight
urban
model
(DLUPLF)
proposed,
enhancing
LSTM
networks
with
contrastive
loss
in
short-term
sampling,
a
difference
compensation
mechanism,
feature
extraction
layer
reduce
costs.
The
adjusts
predictions
learning
differences
employs
dynamic
class-center
regularization.
Its
performance
was
evaluated
through
parameter
tuning
comparative
analysis.
Results
DLUPLF
demonstrated
improved
accuracy
imbalanced
reducing
computational
It
preserved
characteristics
outperformed
traditional
efficiency
prediction
accuracy.
Discussion
effectively
addresses
complexity
challenges,
making
it
promising
solution
forecasting.
Future
work
will
focus
on
real-time
applications
broader
systems.
Sustainability,
Journal Year:
2025,
Volume and Issue:
17(5), P. 2250 - 2250
Published: March 5, 2025
Hydrology
relates
to
many
complex
challenges
due
climate
variability,
limited
resources,
and
especially,
increased
demands
on
sustainable
management
of
water
soil.
Conventional
approaches
often
cannot
respond
the
integrated
complexity
continuous
change
inherent
in
system;
hence,
researchers
have
explored
advanced
data-driven
solutions.
This
review
paper
revisits
how
artificial
intelligence
(AI)
is
dramatically
changing
most
important
facets
hydrological
research,
including
soil
land
surface
modeling,
streamflow,
groundwater
forecasting,
quality
assessment,
remote
sensing
applications
resources.
In
AI
techniques
could
further
enhance
accuracy
texture
analysis,
moisture
estimation,
erosion
prediction
for
better
management.
Advanced
models
also
be
used
as
a
tool
forecast
streamflow
levels,
therefore
providing
valuable
lead
times
flood
preparedness
resource
planning
transboundary
basins.
quality,
AI-driven
methods
improve
contamination
risk
enable
detection
anomalies,
track
pollutants
assist
treatment
processes
regulatory
practices.
combined
with
open
new
perspectives
monitoring
resources
at
spatial
scale,
from
forecasting
storage
variations.
paper’s
synthesis
emphasizes
AI’s
immense
potential
hydrology;
it
covers
latest
advances
future
prospects
field
ensure
