Journal of Materials Research and Technology,
Journal Year:
2023,
Volume and Issue:
25, P. 1495 - 1536
Published: June 6, 2023
Rice
Husk
ash
(RHA)
utilization
in
concrete
as
a
waste
material
can
contribute
to
the
formation
of
robust
cementitious
matrix
with
utmost
properties.
The
strength
HPC
when
subjected
compression
test
is
determined
by
combination
and
quantity
materials
used
its
production.
Thus,
making
mixed
design
process
challenging
ambiguous.
objective
this
research
forecast
containing
RHA,
using
diverse
range
machine
learning
techniques,
including
both
individual
ensemble
learners
such
bagging
boosting.
This
study
will
cause
significant
implications
for
sustainable
construction
practices
facilitating
development
an
efficient
effective
method
forecasting
HPC.
Individual
(ML)
algorithms
are
incorporated
methods
bagging,
adaptive
boosting,
random
forest
algorithms.
These
techniques
use
create
twenty
different
sub-models.
Afterward,
these
sub-models
train
optimized
achieving
best
possible
value
R2.
were
further
fine-tuned
obtain
In
order
assess
or
evaluate
quality,
reliability,
generalizability
data,
K-Fold
cross-validation
utilized.
Furthermore,
index
measuring
statistical
performance
models
validate
compare
assessment
models.
findings
indicate
that
boosting
enhances
prediction
accuracy
weak
models,
minimum
errors
R2
>
0.92
achieved
decision
tree
forest.
general,
model
learner
(ML).
International Journal of Applied Earth Observation and Geoinformation,
Journal Year:
2023,
Volume and Issue:
122, P. 103401 - 103401
Published: July 14, 2023
Flash
floods
are
among
the
world
most
destructive
natural
disasters,
and
developing
optimum
hybrid
Machine
Learning
(ML)
models
for
flash
flood
susceptibility
(FFS)
modeling
remains
a
challenge.
This
study
proposed
novel
intelligence
algorithms
based
on
of
several
ensemble
ML
(i.e.,
Bagged
Flexible
Discriminant
Analysis
(BAFDA),
Extreme
Gradient
Boosting
(XBG),
Rotation
Forest
(ROF)
Boosted
Generalized
Additive
Model
(BGAM))
wrapper-based
factor
optimization
Recursive
Feature
Elimination
(RFE)
Boruta)
to
improve
accuracy
FFS
mapping
at
Neka-Haraz
watershed
in
Iran.
In
addition,
Random
Search
(RS)
method
is
meta-optimization
developed
hyper-parameters.
considers
20
conditioning
factors
(CgFs)
380
non-flood
locations
create
geospatial
database.
The
performance
each
model
was
evaluated
by
area
under
receiver
operating
characteristic
(ROC)
curve
(AUC)
validation
methods,
such
as
efficiency.
demonstrated
good
performance,
with
BGAM-Boruta
achieving
highest
(AUC
=
0.953,
Efficiency
0.910),
followed
ROF-Boruta
0.952),
ROF-RFE
0.951),
BAFDA-Boruta
0.950),
BGAM-RFE
ROF
0.949),
BGAM
0.948),
BAFDA-RFE
0.943),
XGB-Boruta
BAFDA
0.939),
XGB-RFE
0.938)
XGB
0.911).
model,
regional
coverage
about
46%
high
very
areas.
Moreover,
revealed
that
distance
river,
slope,
rainfall,
altitude,
road
CgFs
significant
this
region.
International Journal of Disaster Risk Reduction,
Journal Year:
2024,
Volume and Issue:
108, P. 104503 - 104503
Published: April 23, 2024
Floods
are
a
widespread
and
damaging
natural
phenomenon
that
causes
harm
to
human
lives,
resources,
property
has
agricultural,
eco-environmental,
economic
impacts.
Therefore,
it
is
crucial
perform
flood
susceptibility
mapping
(FSM)
identify
susceptible
zones
mitigate
reduce
damage.
This
study
assessed
the
damage
caused
by
2022
flash
in
Sindh
identified
flood-susceptible
based
on
frequency
ratio
(FR)
analytical
hierarchy
process
(AHP)
models.
Flood
inventory
maps
were
generated,
containing
150
sampling
points,
which
manually
selected
from
Landsat
imagery.
The
points
split
into
70%
for
training
30%
validating
results.
Furthermore,
fourteen
conditioning
factors
considered
analysis
developing
FSM.
final
FSM
categorized
five
zones,
representing
levels
very
low
high.
results
areas
under
high
Ghotki
(FR
4.42%
AHP
5.66%),
Dadu
21.40%
21.29%),
Sanghar
6.81%
6.78%).
Ultimately,
accuracy
was
evaluated
using
receiver
operating
characteristics
area
curve
method,
resulting
82%,
83%),
91%,
90%),
96%,
95%).
enhances
scientific
understanding
of
impacts
across
diverse
regions
emphasizes
importance
accurate
informed
decision-making.
findings
provide
valuable
insights
supportive
policymakers,
agricultural
planners,
stakeholders
engaged
risk
management
adverse
consequences
floods.
