Sustainable Cities and Society,
Journal Year:
2023,
Volume and Issue:
99, P. 104847 - 104847
Published: Aug. 13, 2023
A
key
role
in
making
cities
resilient
has
been
acknowledged
raising
risk
preparedness
and
awareness
of
urban
communities,
by
appropriate
education
communication
strategies,
which
should
rely
on
innovative
pervasive
tools.
In
this
regard,
an
outstanding
paradigm
shift
is
driven
the
advancement
Virtual
Reality,
can
take
advantage
Serious
Games,
for
helping
individuals
develop
responsive
behaviours
case
both
slow
sudden
disasters
and,
thus,
boosting
effective
human-urban-building
interaction
within
a
wider
process
safety
sustainability.
To
end,
paper
proposes
VR-SGs
training
prototype
multi-hazard
scenarios
open
spaces.
The
integrates
results
from
phenomenological
behavioural
analyses
applied
to
representative
typologies
built
environment.
demonstrated
heat
wave
protection
earthquake
response
through
design
implementation
its
functional
features
–
virtual
environment,
mode,
learning
outcomes
storyline
informative
contents,
including
simulation-based
data
surface
temperatures,
extent
falling
debris
crowd
motion.
final
goal
validate
reliable
flexible
tool
view
wide
replication
contexts
instructing
critical
situations
communicating
mitigation
strategies.
Journal Of Big Data,
Journal Year:
2024,
Volume and Issue:
11(1)
Published: May 6, 2024
Abstract
The
United
Nations’
17
Sustainable
Development
Goals
stress
the
importance
of
global
and
local
efforts
to
address
inequalities
implement
sustainability.
Addressing
complex,
interconnected
sustainability
challenges
requires
a
systematic,
interdisciplinary
approach,
where
technology,
AI,
data-driven
methods
offer
potential
solutions
for
optimizing
resources,
integrating
different
aspects
sustainability,
informed
decision-making.
Sustainability
research
surrounds
various
local,
regional,
challenges,
emphasizing
need
identify
emerging
areas
gaps
AI
models
play
crucial
role.
study
performs
comprehensive
literature
survey
scientometric
semantic
analyses,
categorizes
problems,
discusses
sustainable
use
big
data.
outcomes
analyses
highlight
collaborative
inclusive
that
bridges
regional
differences,
interconnection
topics,
major
themes
related
It
further
emphasizes
significance
developing
hybrid
approaches
combining
techniques,
expert
knowledge
multi-level,
multi-dimensional
Furthermore,
recognizes
necessity
addressing
ethical
concerns
ensuring
data
in
research.
Buildings,
Journal Year:
2024,
Volume and Issue:
14(3), P. 737 - 737
Published: March 8, 2024
Wind,
a
renewable
resource
with
growing
importance
in
the
contemporary
world,
is
considered
capable
tool
for
addressing
some
of
problems
linked
rapid
urbanization,
unsustainable
development,
and
climate
change.
As
such,
understanding
modelling
approaches
to
wind
characteristics
cities
becomes
crucial.
While
prior
reviews
delve
into
advancements
reduced-scale
models
computational
fluid
dynamics
simulations,
there
scant
literature
evaluating
large-scale
spatial
urban
environments.
This
paper
aims
consolidate
by
conducting
systematic
review
PRISMA
protocol
capture
contributions
sustainable
development.
The
reviewed
articles
are
categorized
under
two
distinctive
approaches:
(a)
studies
adopting
morphometric
approach,
encompassing
theoretical
foundations,
input
factors,
computation
methods
(b)
mapping
centering
on
amalgamation
microclimate
analysis.
findings
suggest
that
methodologies
hold
considerable
promise
due
their
straightforward
calculations
interpretability.
Nonetheless,
issues
related
data
precision
accuracy
challenge
validity
these
models.
also
probes
implications
planning
policymaking,
advocating
more
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(3), P. 976 - 976
Published: Jan. 23, 2024
Urban
air
pollution
is
a
pressing
global
issue
driven
by
factors
such
as
swift
urbanization,
population
expansion,
and
heightened
industrial
activities.
To
address
this
challenge,
the
integration
of
Machine
Learning
(ML)
into
smart
cities
presents
promising
avenue.
Our
article
offers
comprehensive
insights
recent
advancements
in
quality
research,
employing
PRISMA
method
cornerstone
for
reviewing
process,
while
simultaneously
exploring
application
frequently
employed
ML
methodologies.
Focusing
on
supervised
learning
algorithms,
study
meticulously
analyzes
data,
elucidating
their
unique
benefits
challenges.
These
techniques,
including
LSTM
(Long
Short-Term
Memory),
RF
(Random
Forest),
ANN
(Artificial
Neural
Networks),
SVR
(Support
Vector
Regression),
are
instrumental
our
quest
cleaner,
healthier
urban
environments.
By
accurately
predicting
key
pollutants
particulate
matter
(PM),
nitrogen
oxides
(NOx),
carbon
monoxide
(CO),
ozone
(O3),
these
methods
offer
tangible
solutions
society.
They
enable
informed
decision-making
planners
policymakers,
leading
to
proactive,
sustainable
strategies
combat
pollution.
As
result,
well-being
health
populations
significantly
improved.
In
revised
abstract,
importance
context
explicitly
emphasized,
underlining
role
improving
environments
enhancing
populations.
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(3), P. 467 - 467
Published: Jan. 25, 2024
Long-term
exposure
to
high
concentrations
of
fine
particles
can
cause
irreversible
damage
people’s
health.
Therefore,
it
is
extreme
significance
conduct
large-scale
continuous
spatial
particulate
matter
(PM2.5)
concentration
prediction
for
air
pollution
prevention
and
control
in
China.
The
distribution
PM2.5
ground
monitoring
stations
China
uneven
with
a
larger
number
southeastern
China,
while
the
sites
also
insufficient
quality
control.
Remote
sensing
technology
obtain
information
quickly
macroscopically.
possible
predict
based
on
multi-source
remote
data.
Our
study
took
as
research
area,
using
Pearson
correlation
coefficient
GeoDetector
select
auxiliary
variables.
In
addition,
long
short-term
memory
neural
network
random
forest
regression
model
were
established
estimation.
We
finally
selected
(R2
=
0.93,
RMSE
4.59
μg
m−3)
our
by
evaluation
index.
across
2021
was
estimated,
then
influence
factors
high-value
regions
explored.
It
clear
that
not
only
related
local
geographical
meteorological
conditions,
but
closely
economic
social
development.
Coasts,
Journal Year:
2024,
Volume and Issue:
4(1), P. 127 - 149
Published: Feb. 26, 2024
Mapping
coastal
regions
is
important
for
environmental
assessment
and
monitoring
spatio-temporal
changes.
Although
traditional
cartographic
methods
using
a
geographic
information
system
(GIS)
are
applicable
in
image
classification,
machine
learning
(ML)
present
more
advantageous
solutions
pattern-finding
tasks
such
as
the
automated
detection
of
landscape
patches
heterogeneous
landscapes.
This
study
aimed
to
discriminate
patterns
along
eastern
coasts
Mozambique
ML
modules
Geographic
Resources
Analysis
Support
System
(GRASS)
GIS.
The
random
forest
(RF)
algorithm
module
‘r.learn.train’
was
used
map
landscapes
shoreline
Bight
Sofala,
remote
sensing
(RS)
data
at
multiple
temporal
scales.
dataset
included
Landsat
8-9
OLI/TIRS
imagery
collected
dry
period
during
2015,
2018,
2023,
which
enabled
evaluation
dynamics.
supervised
classification
RS
rasters
supported
by
Scikit-Learn
package
Python
embedded
GRASS
Sofala
characterized
diverse
marine
ecosystems
dominated
swamp
wetlands
mangrove
forests
located
mixed
saline–fresh
waters
coast
Mozambique.
paper
demonstrates
advantages
areas.
integration
Earth
Observation
data,
processed
decision
tree
classifier
land
cover
characteristics
recent
changes
ecosystem
Mozambique,
East
Africa.
