Machine Learning in Geosciences: A Review of Complex Environmental Monitoring Applications
Machine Learning and Knowledge Extraction,
Journal Year:
2024,
Volume and Issue:
6(2), P. 1263 - 1280
Published: June 5, 2024
This
is
a
systematic
literature
review
of
the
application
machine
learning
(ML)
algorithms
in
geosciences,
with
focus
on
environmental
monitoring
applications.
ML
algorithms,
their
ability
to
analyze
vast
quantities
data,
decipher
complex
relationships,
and
predict
future
events,
they
offer
promising
capabilities
implement
technologies
based
more
precise
reliable
data
processing.
considers
several
vulnerable
particularly
at-risk
themes
as
landfills,
mining
activities,
protection
coastal
dunes,
illegal
discharges
into
water
bodies,
pollution
degradation
soil
matrices
large
industrial
complexes.
These
case
studies
about
provide
an
opportunity
better
examine
impact
human
activities
environment,
specific
matrices.
The
recent
underscores
increasing
importance
these
contexts,
highlighting
preference
for
adapted
classic
models:
random
forest
(RF)
(the
most
widely
used),
decision
trees
(DTs),
support
vector
machines
(SVMs),
artificial
neural
networks
(ANNs),
convolutional
(CNNs),
principal
component
analysis
(PCA),
much
more.
In
field
management,
following
methodologies
invaluable
insights
that
can
steer
strategic
planning
decision-making
accurate
image
classification,
prediction
models,
object
detection
recognition,
map
variable
predictions.
Language: Английский
Isolation Forest for Environmental Monitoring: A Data-Driven Approach to Land Management
Environments,
Journal Year:
2025,
Volume and Issue:
12(4), P. 116 - 116
Published: April 10, 2025
This
paper
examines
land
management
technologies
to
enhance
environmental
monitoring
more
efficiently.
The
study
highlights
the
interactions
between
human
activities
and
systems
with
a
data-driven
approach.
There
are
many
pressures,
such
as
pollution,
degradation,
habitat
loss,
negatively
impacting
soil
health.
methodology
proposed
improves
status
assessments
in
response
evolving
pressures
by
utilizing
satellite
imagery
predictive
modeling.
integration
of
Sentinel-2
imagery,
calculation
various
spectral
indices
(NDVI,
NBR,
NDMI,
EVI,
SAVI)
at
different
time
intervals,
application
Isolation
Forest
algorithm
employed
this
determine
specific
area
that
is
affected
issue.
chosen
was
favored
due
its
superior
performance
handling
high-dimensionality
data,
enhanced
computational
efficiency,
provision
interpretable
results,
insensitivity
disparities
class
distribution.
analyzes
two
separate
cases
scales.
first
involves
wildfire
identification
achieving
an
overall
accuracy
98%.
second
focuses
on
expansion
areas
pre-existing
quarries
95%.
NBR
proved
most
effective
delineating
burned
areas,
whereas
EVI
generated
remarkable
results
quarry
case
study.
approach
provides
scalable
tool
for
monitoring,
supporting
sustainable
policies,
strengthening
ecosystem
resilience.
Language: Английский
Assessment of the Risk to Human Health and Pollution Levels Due to the Presence of Metal(loid)s in Sediments, Water, and Fishes in Urban Rivers in the State of Mato Grosso do Sul, Brazil
Urban Science,
Journal Year:
2025,
Volume and Issue:
9(4), P. 114 - 114
Published: April 5, 2025
This
study
aimed
to
assess
the
pollution
levels,
sources,
ecological
risk,
and
human
health
risks
of
metal(loid)s
in
water,
sediment,
muscle
tissue
Prochilodus
lineatus
Pimelodus
maculatus
from
rivers
state
Mato
Grosso
do
Sul,
Brazil.
The
content
river
fish
were
determined
by
inductively
coupled
plasma
optical
emission
spectrometry.
Sediment
assessment
was
carried
out
geo-accumulation
index,
contamination
factor,
enrichment
load
index.
There
significant
differences
concentration
values
for
Al,
As,
Cd,
Co,
Cr,
Cu,
Mo,
Ni,
Pb,
Hg.
greater
tendency
elements
N,
Hg,
Co
waters
Anhanduí
River
2020
Cr
Pb
2021.
concentrations
Hg
are
above
permitted
limit
heavy
metal
ions
drinking
water
established
WHO.
metals
sediments
set
Conama/Brazil
other
countries.
very
highly
contaminated
Cd
with
moderate
Pb.
All
showed
a
decline
site
quality,
which
indicates
that
it
is
polluted.
Sediments
classified
severe
Mo.
Al
highest
P.
relation
analyzed.
also
presence
such
as
tissues
species.
Therefore,
these
concern
due
consumption
fish,
since
there
carcinogenic
risk
related
mainly
As
Cd.
Language: Английский