Advances in environmental engineering and green technologies book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 49 - 64
Published: Jan. 10, 2025
This
chapter
examines
the
transformative
role
of
cloud
computing
and
IoT
in
advancing
wildlife
conservation
initiatives.
As
technological
advancements
redefine
our
capabilities,
they
provide
innovative
tools
for
monitoring,
tracking,
safeguarding
endangered
species.
highlights
cutting-edge
solutions
that
utilize
cloud-based
platforms
devices
to
revolutionize
practices.
It
explores
real-time
animal
data-driven
anti-poaching
measures,
other
groundbreaking
approaches
are
reshaping
efforts
preserve
biodiversity
ensure
ecosystem
sustainability.
Journal of Contemporary Administration and Management (ADMAN),
Journal Year:
2023,
Volume and Issue:
1(2), P. 47 - 53
Published: Aug. 13, 2023
In
this
modern
era,
technological
innovation
has
become
one
of
the
main
keys
in
improving
efficiency,
productivity,
and
competitiveness
a
nation.
On
other
hand,
awareness
importance
environmental
sustainability
is
increasing,
given
challenges
such
as
climate
change,
natural
resource
depletion,
negative
impacts
pollution.
The
purpose
research
to
analyse
role
public
policy
promoting
sustainability.
current
type
qualitative.
Data
collection
techniques
include
listening
recording
important
information
conduct
data
analysis
through
reduction,
display,
conclusion
drawing.
study
results
show
that
encouraging
very
achieving
sustainable
advanced
future.
right
policies
can
create
an
enabling
environment
for
innovation,
stimulate
development,
empower
human
resources
face
rapid
challenges.
Advances in environmental engineering and green technologies book series,
Journal Year:
2023,
Volume and Issue:
unknown, P. 71 - 98
Published: June 9, 2023
This
chapter
explores
the
use
of
AI
in
water
treatment,
evaporation
management,
and
resource
management.
It
begins
with
an
introduction,
highlighting
AI's
motivation
objectives.
The
then
discusses
applications,
challenges,
opportunities
their
implementation.
compares
traditional
approaches
AI-driven
solutions
for
control
optimization
presents
case
studies
applications
to
demonstrate
real-world
examples.
also
management
data-driven
modeling,
forecasting,
optimization,
decision
support
systems.
benefits
limitations
AI,
interdisciplinary
collaboration,
ethical
considerations,
policy
frameworks
responsible
provides
recommendations
future
research
advance
treatment
Neurocomputing,
Journal Year:
2024,
Volume and Issue:
599, P. 128096 - 128096
Published: June 22, 2024
Green
artificial
intelligence
(AI)
is
more
environmentally
friendly
and
inclusive
than
conventional
AI,
as
it
not
only
produces
accurate
results
without
increasing
the
computational
cost
but
also
ensures
that
any
researcher
with
a
laptop
can
perform
high-quality
research
need
for
costly
cloud
servers.
This
paper
discusses
green
AI
pivotal
approach
to
enhancing
environmental
sustainability
of
systems.
Described
are
solutions
eco-friendly
practices
in
other
fields
(green-by
AI),
strategies
designing
energy-efficient
machine
learning
(ML)
algorithms
models
(green-in
tools
accurately
measuring
optimizing
energy
consumption.
Also
examined
role
regulations
promoting
future
directions
sustainable
ML.
Underscored
importance
aligning
considerations,
fostering
eco-conscious
World Journal of Advanced Research and Reviews,
Journal Year:
2024,
Volume and Issue:
21(1), P. 161 - 171
Published: Jan. 4, 2024
The
rapid
increase
in
human
activities
is
causing
significant
damage
to
our
planet's
ecosystems,
necessitating
innovative
solutions
preserve
biodiversity
and
counteract
ecological
threats.
Artificial
Intelligence
(AI)
has
emerged
as
a
transformative
force,
providing
unparalleled
capabilities
for
environmental
monitoring
conservation.
This
research
paper
explores
the
applications
of
AI
ecosystem
management,
including
wildlife
tracking,
habitat
assessment,
analysis,
natural
disaster
prediction.
AI's
role
conservation
includes
resource
conservation,
species
identification.
algorithms
analyze
camera
trap
footage,
drone
imagery,
GPS
data
identify
estimate
population
sizes,
leading
improved
anti-poaching
efforts
enhanced
protection
diverse
species.
Habitat
assessment
involve
AI-powered
image
which
aids
assessing
forest
health,
detecting
deforestation,
identifying
areas
need
restoration.
Biodiversity
analysis
identification
are
achieved
through
that
acoustic
recordings,
DNA
(eDNA),
footage.
These
innovations
different
species,
assess
levels,
even
discover
new
or
endangered
flood
prediction
systems
provide
early
warnings,
empowering
communities
with
better
preparedness
evacuation
efforts.
Challenges,
such
quality
availability,
algorithmic
bias,
infrastructure
limitations,
acknowledged
opportunities
growth
improvement.
In
policy
regulation,
advocates
clear
frameworks
prioritizing
privacy
security,
transparency,
equitable
access.
Responsible
development
ethical
use
emphasized
foundational
pillars,
ensuring
integration
into
aligns
principles
fairness,
societal
benefit.
Practice, progress, and proficiency in sustainability,
Journal Year:
2024,
Volume and Issue:
unknown, P. 485 - 506
Published: Aug. 27, 2024
Artificial
Intelligence
plays
a
pivotal
in
resolving
climate
change
and
the
environmental
crisis
with
help
of
AI
technologies.
However,
by
scrubbing
massive
amounts
information
from
satellites
sensors,
it
can
refine
prediction
allowing
for
better
re-prioritization
downstream
when
initiating
mitigation
plans.
In
addition,
using
Intelligence,
also
optimizes
trees
autonomous
networks
energy
systems,
emissions
reduction
carbon.
But
training
is
intensive,
responsible
sizable
chunk
greenhouse
gas
emissions.
As
evolves,
essential
to
guide
its
development
deployment
principles
sustainability
responsibility.
This
chapter
examines
various
aspects
issues
achieve
sustainable
goals.
It
significant
challenges
limitations
intertwined
incorporation
degradation
crises
change.
Practice, progress, and proficiency in sustainability,
Journal Year:
2024,
Volume and Issue:
unknown, P. 109 - 130
Published: Aug. 27, 2024
Concern
about
the
environment
has
long
sparked
public
outcry,
debate,
and
awareness
campaigns,
which
in
turn
have
piqued
people's
curiosity
new
technology
like
AI.
While
addressing
environmental
sustainability
concerns
is
critical,
rise
of
AI
made
it
possible
to
prioritise
human
interests
while
solving
majority
common
problems.
A
sustainable
future
takes
into
account
interconnectedness
ecological,
social,
economic
factors.
There
are
many
problems,
such
as
climate
change
degradation,
that
call
for
innovative
smart
solutions.
Artificial
intelligence
(AI)
literature
cover
a
wide
range
topics
accomplish
Sustainable
Development
Goals
(SDGs),
this
study
will
investigate
potential
uses
artificial
solve
problems
different
industries.
Nature Communications,
Journal Year:
2023,
Volume and Issue:
14(1)
Published: Oct. 17, 2023
Tropical
forest
recovery
is
fundamental
to
addressing
the
intertwined
climate
and
biodiversity
loss
crises.
While
regenerating
trees
sequester
carbon
relatively
quickly,
pace
of
remains
contentious.
Here,
we
use
bioacoustics
metabarcoding
measure
post-agriculture
in
a
global
hotspot
Ecuador.
We
show
that
community
composition,
not
species
richness,
vocalizing
vertebrates
identified
by
experts
reflects
restoration
gradient.
Two
automated
measures
-
an
acoustic
index
model
bird
composition
derived
from
independently
developed
Convolutional
Neural
Network
correlated
well
with
(adj-R²
=
0.62
0.69,
respectively).
Importantly,
both
reflected
non-vocalizing
nocturnal
insects
via
metabarcoding.
such
monitoring
tools,
based
on
new
technologies,
can
effectively
monitor
success
recovery,
using
robust
reproducible
data.
Biology,
Journal Year:
2023,
Volume and Issue:
12(10), P. 1298 - 1298
Published: Sept. 30, 2023
This
review
discusses
the
transformative
potential
of
integrating
multi-omics
data
and
artificial
intelligence
(AI)
in
advancing
horticultural
research,
specifically
plant
phenotyping.
The
traditional
methods
phenotyping,
while
valuable,
are
limited
their
ability
to
capture
complexity
biology.
advent
(meta-)genomics,
(meta-)transcriptomics,
proteomics,
metabolomics
has
provided
an
opportunity
for
a
more
comprehensive
analysis.
AI
machine
learning
(ML)
techniques
can
effectively
handle
volume
data,
providing
meaningful
interpretations
predictions.
Reflecting
multidisciplinary
nature
this
area
review,
readers
will
find
collection
state-of-the-art
solutions
that
key
integration
phenotyping
experiments
horticulture,
including
experimental
design
considerations
with
several
technical
non-technical
challenges,
which
discussed
along
solutions.
future
prospects
include
precision
predictive
breeding,
improved
disease
stress
response
management,
sustainable
crop
exploration
biodiversity.
holds
immense
promise
revolutionizing
research
applications,
heralding
new
era
