Environmental Data Science,
Journal Year:
2025,
Volume and Issue:
4
Published: Jan. 1, 2025
Abstract
Prediction
of
dynamic
environmental
variables
in
unmonitored
sites
remains
a
long-standing
challenge
for
water
resources
science.
The
majority
the
world’s
freshwater
have
inadequate
monitoring
critical
needed
management.
Yet,
need
to
widespread
predictions
hydrological
such
as
river
flow
and
quality
has
become
increasingly
urgent
due
climate
land
use
change
over
past
decades,
their
associated
impacts
on
resources.
Modern
machine
learning
methods
outperform
process-based
empirical
model
counterparts
hydrologic
time
series
prediction
with
ability
extract
information
from
large,
diverse
data
sets.
We
review
relevant
state-of-the
art
applications
streamflow,
quality,
other
discuss
opportunities
improve
emerging
incorporating
watershed
characteristics
process
knowledge
into
classical,
deep
learning,
transfer
methodologies.
analysis
here
suggests
most
prior
efforts
been
focused
frameworks
built
many
at
daily
scales
United
States,
but
that
comparisons
between
different
classes
are
few
inadequate.
identify
several
open
questions
include
inputs
site
characteristics,
mechanistic
understanding
spatial
context,
explainable
AI
techniques
modern
frameworks.
This
review
explores
the
application
of
machine
learning
in
predicting
and
optimizing
key
physicochemical
properties
deep
eutectic
solvents,
including
CO2
solubility,
density,
electrical
conductivity,
heat
capacity,
melting
temperature,
surface
tension,
viscosity.
By
leveraging
learning,
researchers
aim
to
enhance
understanding
customization
a
critical
step
expanding
their
use
across
various
industrial
research
domains.
The
integration
represents
significant
advancement
tailoring
solvents
for
specific
applications,
marking
progress
toward
development
greener
more
efficient
processes.
As
continues
unlock
full
potential
it
is
expected
play
an
increasingly
pivotal
role
revolutionizing
sustainable
chemistry
driving
innovations
environmental
technology.
Journal of Marine Science and Engineering,
Journal Year:
2025,
Volume and Issue:
13(2), P. 199 - 199
Published: Jan. 22, 2025
This
study
explores
the
use
of
Temporal
Fusion
Transformers
(TFTs),
an
AI/ML
technique,
to
enhance
prediction
coastal
dynamics
along
Western
Black
Sea
coast.
We
integrate
in-situ
observations
from
five
meteo-oceanographic
stations
with
modelled
geospatial
marine
data
Copernicus
Marine
Service.
TFTs
are
employed
refine
predictions
shallow
water
by
considering
atmospheric
influences,
a
particular
focus
on
wave-wind
correlations
in
regions.
Atmospheric
pressure
and
temperature
treated
as
latitude-dependent
constants,
specific
investigations
into
extreme
events
like
freezing
solar
radiation-induced
turbulence.
Explainable
AI
(XAI)
is
exploited
ensure
transparent
model
interpretations
identify
key
influential
input
variables.
Data
attribution
strategies
address
missing
concerns,
while
ensemble
modelling
enhances
overall
robustness.
The
models
demonstrate
significant
improvement
accuracy
compared
traditional
methods.
research
provides
deeper
understanding
atmosphere-marine
interactions
demonstrates
efficacy
Artificial
intelligence
(AI)/Machine
Learning
(ML)
bridging
observational
gaps
for
informed
zone
management
decisions,
essential
maritime
safety
Environmental Data Science,
Journal Year:
2025,
Volume and Issue:
4
Published: Jan. 1, 2025
Abstract
Prediction
of
dynamic
environmental
variables
in
unmonitored
sites
remains
a
long-standing
challenge
for
water
resources
science.
The
majority
the
world’s
freshwater
have
inadequate
monitoring
critical
needed
management.
Yet,
need
to
widespread
predictions
hydrological
such
as
river
flow
and
quality
has
become
increasingly
urgent
due
climate
land
use
change
over
past
decades,
their
associated
impacts
on
resources.
Modern
machine
learning
methods
outperform
process-based
empirical
model
counterparts
hydrologic
time
series
prediction
with
ability
extract
information
from
large,
diverse
data
sets.
We
review
relevant
state-of-the
art
applications
streamflow,
quality,
other
discuss
opportunities
improve
emerging
incorporating
watershed
characteristics
process
knowledge
into
classical,
deep
learning,
transfer
methodologies.
analysis
here
suggests
most
prior
efforts
been
focused
frameworks
built
many
at
daily
scales
United
States,
but
that
comparisons
between
different
classes
are
few
inadequate.
identify
several
open
questions
include
inputs
site
characteristics,
mechanistic
understanding
spatial
context,
explainable
AI
techniques
modern
frameworks.
