Assessing Pan-Canada wildfire susceptibility by integrating satellite data with novel hybrid deep learning and black widow optimizer algorithms
Khabat Khosravi,
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Ashkan Mosallanejad,
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Sayed M. Bateni
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et al.
The Science of The Total Environment,
Journal Year:
2025,
Volume and Issue:
977, P. 179369 - 179369
Published: April 15, 2025
In
light
of
the
rising
frequency
severe
wildfires
and
their
widespread
socio-ecological
impacts,
it
is
essential
to
develop
cost-effective
reliable
methods
for
accurately
predicting
mapping
wildfire
occurrences.
This
study
aimed
several
novel
deep-learning
models
determine
probability
occurrence
on
a
national
scale
in
Canada
by
integrating
remote
sensing
data,
deep
learning,
metaheuristic
algorithms.
present
study,
standalone
long
short-term
memory
(LSTM),
recurrent
neural
network
(RNN),
bidirectional
LSTM
(BiLSTM),
RNN
(BiRNN)
were
developed,
these
hybridized
with
black
widow
optimizer
(BWO).
To
train
test
models,
4240
historical
(2014-2023)
large
locations
collected
across
Canada.
Fourteen
wildfire-related
predictors
used
map
susceptibility,
Gini
coefficient
determining
each
predictor's
importance
occurrence.
Finally,
developed
evaluated
tested
using
area
under
receiver
operating
characteristic
curve
(AUC),
other
statistical
error
metrics.
During
testing
stage,
hybrid
BiLSTM-BWO
model
outperformed
(AUC
=
0.9686),
followed
RNN-BWO
0.9683),
LSTM-BWO
0.9672),
BiRNN-BWO
0.9643),
BiLSTM
0.9420),
0.9367),
BiRNN
0.9247)
0.8737).
Based
model,
19.7
%,
42.6
13.4
14.5
9.8
%
was
classified
as
having
very
low,
moderate,
high,
high
susceptibility
future
wildfires,
respectively.
Saskatchewan,
Manitoba,
British
Columbia
Alberta
among
provinces
areas
while
Prince
Edward
Island
Newfoundland
Labrador
from
Atlantic
had
lowest
According
coefficient,
windspeed,
land
use
cover,
precipitation,
specific
humidity
maximum
temperature
strongest
impact
highlights
effectiveness
prediction
potential
improve
management,
prevention,
mitigation
strategies
Canada's
future.
Language: Английский
Machine Learning and Deep Learning for Wildfire Spread Prediction: A Review
Henintsoa S. Andrianarivony,
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Moulay A. Akhloufi
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Fire,
Journal Year:
2024,
Volume and Issue:
7(12), P. 482 - 482
Published: Dec. 18, 2024
The
increasing
frequency
and
intensity
of
wildfires
highlight
the
need
to
develop
more
efficient
tools
for
firefighting
management,
particularly
in
field
wildfire
spread
prediction.
Classical
models
have
relied
on
mathematical
empirical
approaches,
which
trouble
capturing
complexity
fire
dynamics
suffer
from
poor
flexibility
static
assumptions.
emergence
machine
learning
(ML)
and,
specifically,
deep
(DL)
has
introduced
new
techniques
that
significantly
enhance
prediction
accuracy.
ML
models,
such
as
support
vector
machines
ensemble
use
tabular
data
points
identify
patterns
predict
behavior.
However,
these
often
struggle
with
dynamic
nature
wildfires.
In
contrast,
DL
convolutional
neural
networks
(CNNs)
recurrent
(CRNs),
excel
at
handling
spatiotemporal
complexities
data.
CNNs
are
effective
analyzing
spatial
satellite
imagery,
while
CRNs
suited
both
sequential
data,
making
them
highly
performant
predicting
This
paper
presents
a
systematic
review
recent
developed
prediction,
detailing
commonly
used
datasets,
improvements
achieved,
limitations
current
methods.
It
also
outlines
future
research
directions
address
challenges,
emphasizing
potential
play
an
important
role
management
mitigation
strategies.
Language: Английский