Sustainable Cities and Society,
Год журнала:
2024,
Номер
113, С. 105654 - 105654
Опубликована: Июль 9, 2024
Ensuring
sustainable
water
and
electricity
consumption
in
urban
residential
buildings
is
a
growing
challenge
worldwide,
particularly
rapidly
developing
regions
with
harsh
climates.
This
study
examines
the
seasonal
variation
of
Doha,
Qatar,
exploring
interconnectedness
land
use/land
cover
(LULC)
socio-demographic
characteristics
household
consumption.
For
this
purpose,
we
employed
statistical
analysis
(i.e.
Pearson
correlation
Bootstrap
analysis)
advanced
geostatistical
models,
including
Geographically
Weighted
Regression
(GWR)
Multiscale
(MGWR),
to
analyze
monitor
spatial
variations
The
methods
involved
assessing
relationship
between
surface
temperature
(LST),
water-electricity
consumption,
analyzing
impact
demographic
variables.
Key
findings
indicate
significant
spatiotemporal
influenced
by
changes
LULC
such
as
size
structure.
highlight
need
for
integrated
planning
energy
policies
that
consider
impacts
enhance
efficiency
sustainability
settings.
Furthermore,
results
underscore
importance
addressing
complex
interplay
development
resource
policy-making.
Sensors,
Год журнала:
2025,
Номер
25(4), С. 1169 - 1169
Опубликована: Фев. 14, 2025
This
study
introduces
an
innovative
machine
learning
method
to
model
the
spatial
variation
of
land
surface
temperature
(LST)
with
a
focus
on
urban
center
Da
Nang,
Vietnam.
Light
Gradient
Boosting
Machine
(LightGBM),
support
vector
machine,
random
forest,
and
Deep
Neural
Network
are
employed
establish
functional
relationships
between
LST
its
influencing
factors.
The
approaches
trained
validated
using
remote
sensing
data
from
2014,
2019,
2024.
Various
explanatory
variables
representing
topographical
characteristics,
as
well
landscapes,
used.
Experimental
results
show
that
LightGBM
outperforms
other
benchmark
methods.
In
addition,
Shapley
Additive
Explanations
utilized
clarify
impact
factors
affecting
LST.
analysis
outcomes
indicate
while
importance
these
changes
over
time,
density
greenspace
consistently
emerge
most
influential
attained
R2
values
0.85,
0.92,
0.91
for
years
2024,
respectively.
findings
this
work
can
be
helpful
deeper
understanding
heat
stress
dynamics
facilitate
planning.
Energy and Built Environment,
Год журнала:
2024,
Номер
unknown
Опубликована: Июнь 1, 2024
Extreme
heat
due
to
changing
climate
poses
a
new
challenge
for
temperate
climates.
The
is
further
aggravated
by
inadequate
research,
policy,
or
preparedness
effectively
respond
and
recover
from
its
impacts.
While
urban
morphology
plays
crucial
role
in
mitigating
heat,
it
has
received
limited
attention
planning,
highlighting
the
need
exploration,
particularly
regions.
To
illustrate
potential
mitigations,
we
use
example
of
coastal
city
Cardiff.
establish
interrelations
between
island
patterns,
explored
spatiotemporal
variations
land
surface
temperature
(LST),
normalised
difference
vegetation
index
(NDVI),
(SUHI)
local
zone
(LCZ)
classification
Results
showed
significant
variation
SUHI
LCZ
zones.
Both
LST
NDVI
were
found
vary
significantly
across
zones
demonstrating
their
association
with
form
locality.
For
built-up
areas,
more
compact
built-environment
smaller
cover
larger
building
density
was
2.0°C
warmer
than
open
when
comparing
mean
summer
LSTs.
On
average,
natural
classes
exhibit
that
8.0°C
lower
6.0°C
built-environment.
Consequently,
high-density,
LCZs
have
greater
effect
compared
classes.
Therefore,
cities
will
benefit
incorporating
an
sufficient
greenery
spaces.
These
findings
help
determine
optimal
climates
develop
mitigation
strategies
while
designing,
improving
existing
areas.
In
addition,
map
applied
this
study
Cardiff
enable
international
comparison
testing
proven
change
adaptation
techniques
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