Geomatics Natural Hazards and Risk,
Год журнала:
2024,
Номер
15(1)
Опубликована: Май 28, 2024
Frequent
floods
caused
by
monsoons
and
rainstorms
have
significantly
affected
the
resilience
of
human
natural
ecosystems
in
Nam
Ngum
River
Basin,
Lao
PDR.
A
cost-efficient
framework
integrating
advanced
remote
sensing
machine
learning
techniques
is
proposed
to
address
this
issue
enhancing
flood
susceptibility
understanding
informed
decision-making.
This
study
utilizes
geo-datasets
algorithms
(Random
Forest,
Support
Vector
Machine,
Artificial
Neural
Networks,
Long
Short-Term
Memory)
generate
comprehensive
maps.
The
results
highlight
Random
Forest's
superior
performance,
achieving
highest
train
test
Area
Under
Curve
Receiver
Operating
Characteristic
(AUROC)
(1.00
0.993),
accuracy
(0.957),
F1-score
(0.962),
kappa
value
(0.914),
with
lowest
mean
squared
error
(0.207)
Root
Mean
Squared
Error
(0.043).
Vulnerability
particularly
pronounced
low-elevation
low-slope
southern
downstream
areas
(Central
part
PDR).
reveal
that
36%–53%
basin's
total
area
highly
susceptible
flooding,
emphasizing
dire
need
for
coordinated
floodplain
management
strategies.
research
uses
freely
accessible
data,
addresses
data
scarcity
studies,
provides
valuable
insights
disaster
risk
sustainable
planning
Results in Engineering,
Год журнала:
2023,
Номер
21, С. 101665 - 101665
Опубликована: Дек. 12, 2023
Climate
change
and
flooding
are
related
issues
on
the
Earth's
surface,
while
numerous
lowland
areas,
especially
delta
regions,
mostly
affected
by
flood
hazards.
Hence,
susceptibility
mapping
simulation
of
future
effect
areas
essential
for
hazard
management
awareness.
The
river
floodplain
Ganga
River
in
Bihar
state
most
due
to
high
annual
floods.
Floods
cause
huge
economic
losses
environmental
degradation,
such
as
deforestation,
riverbank
erosion,
water
quality
loss.
Thus,
vulnerability
measurement
is
a
serious
concern
this
area,
which
involves
building
proper
awareness
mitigation
strategies
achieve
sustainable
development
goals.
Remote
Sensing
(RS)
widely
applied
hydrological
issues.
statistical
approaches,
Analytical
Hierarchy
Process
(AHP),
Frequency
Ratio
(FR),
Fuzzy-AHP
(FAHP)
algorithms,
were
analysis
selected
plain
state.
suitable
three
different
approaches
9604.21
km2
9712.48
9598.28
channel
not
area.
flooded
maps
indicated
lands
using
Google
Earth
Engine
(GEE)
years
2977.69
(2020),
10481.63
(2021),
1103.89
(2022),
respectively.
results
current
study
indicate
that
area
essentially
need
attention
adaptation
reduction
addition
socio-economic
variability
monsoon
regions.
Otherwise,
floods
destroyed
cropland,
increased
food
scarcity,
caused
losses.
Remote Sensing,
Год журнала:
2022,
Номер
14(24), С. 6229 - 6229
Опубликована: Дек. 8, 2022
Twenty-two
flood-causative
factors
were
nominated
based
on
morphometric,
hydrological,
soil
permeability,
terrain
distribution,
and
anthropogenic
inferences
further
analyzed
through
the
novel
hybrid
machine
learning
approach
of
random
forest,
support
vector
machine,
gradient
boosting,
naïve
Bayes,
decision
tree
(ML)
models.
A
total
400
flood
nonflood
locations
acted
as
target
variables
hazard
zoning
map.
All
operative
in
this
study
tested
using
variance
inflation
factor
(VIF)
values
(<5.0)
Boruta
feature
ranking
(<10
ranks)
for
FHZ
maps.
The
model
along
with
RF
GBM
had
sound
maps
area.
area
under
receiver
operating
characteristics
(AUROC)
curve
statistical
matrices
such
accuracy,
precision,
recall,
F1
score,
gain
lift
applied
to
assess
performance.
70%:30%
sample
ratio
training
validation
standalone
models
concerning
AUROC
value
showed
results
all
ML
models,
(97%),
SVM
(91%),
NB
(96%),
DT
(88%),
(97%).
also
suitability
RF,
GBM,
developing
