Journal of Water and Climate Change,
Journal Year:
2022,
Volume and Issue:
14(1), P. 200 - 222
Published: Dec. 19, 2022
Abstract
The
objective
of
this
study
was
the
development
an
approach
based
on
machine
learning
and
GIS,
namely
Adaptive
Neuro-Fuzzy
Inference
System
(ANFIS),
Gradient-Based
Optimizer
(GBO),
Chaos
Game
Optimization
(CGO),
Sine
Cosine
Algorithm
(SCA),
Grey
Wolf
(GWO),
Differential
Evolution
(DE)
to
construct
flood
susceptibility
maps
in
Ha
Tinh
province
Vietnam.
database
includes
13
conditioning
factors
1,843
locations,
which
were
split
by
a
ratio
70/30
between
those
used
build
validate
model,
respectively.
Various
statistical
indices,
root
mean
square
error
(RMSE),
area
under
curve
(AUC),
absolute
(MAE),
accuracy,
R1
score,
applied
models.
results
show
that
all
proposed
models
performed
well,
with
AUC
value
more
than
0.95.
Of
models,
ANFIS-GBO
most
accurate,
0.96.
Analysis
shows
approximately
32–38%
is
located
high
very
zone.
successful
performance
over
large-scale
can
help
local
authorities
decision-makers
develop
policies
strategies
reduce
threats
related
flooding
future.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
Journal Year:
2022,
Volume and Issue:
15, P. 2341 - 2356
Published: Jan. 1, 2022
Regions
around
the
world
experience
adverse
climate-change-induced
conditions
that
pose
severe
risks
to
normal
and
sustainable
operations
of
modern
societies.
Extreme
weather
events,
such
as
floods,
rising
sea
levels,
storms,
stand
characteristic
examples
impair
core
services
global
ecosystem.
Especially
floods
have
a
impact
on
human
activities,
hence,
early
accurate
delineation
disaster
is
top
priority
since
it
provides
environmental,
economic,
societal
benefits
eases
relief
efforts.
In
this
article,
we
introduce
OmbriaNet,
deep
neural
network
architecture,
based
convolutional
networks,
detects
changes
between
permanent
flooded
water
areas
by
exploiting
temporal
differences
among
flood
events
extracted
different
sensors.
To
demonstrate
potential
proposed
approach,
generated
OMBRIA,
bitemporal
multimodal
satellite
imagery
dataset
for
image
segmentation
through
supervised
binary
classification.
It
consists
total
number
3.376
images,
synthetic
aperture
radar
from
Sentinel-1,
multispectral
Sentinel-2,
accompanied
with
ground-truth
images
produced
data
derived
experts
provided
Emergency
Management
Service
European
Space
Agency
Copernicus
Program.
The
covers
23
globe,
2017
2021.
We
collected,
co-registrated
preprocessed
in
Google
Earth
Engine.
validate
performance
our
method,
performed
benchmarking
experiments
OMBRIA
compared
several
competitive
state-of-the-art
techniques.
experimental
analysis
demonstrated
formulation
able
produce
high-quality
maps,
achieving
superior
over
state-of-the-art.
provide
dataset,
well
OmbriaNet
code
at:
https://github.com/geodrak/OMBRIA
.
Remote Sensing,
Journal Year:
2022,
Volume and Issue:
14(21), P. 5515 - 5515
Published: Nov. 2, 2022
Floods,
one
of
the
most
common
natural
hazards
globally,
are
challenging
to
anticipate
and
estimate
accurately.
This
study
aims
demonstrate
predictive
ability
four
ensemble
algorithms
for
assessing
flood
risk.
Bagging
(BE),
logistic
model
tree
(LT),
kernel
support
vector
machine
(k-SVM),
k-nearest
neighbour
(KNN)
used
in
this
zoning
Jeddah
City,
Saudi
Arabia.
The
141
locations
have
been
identified
research
area
based
on
interpretation
aerial
photos,
historical
data,
Google
Earth,
field
surveys.
For
purpose,
14
continuous
factors
different
categorical
examine
their
effect
flooding
area.
dependency
analysis
(DA)
was
analyse
strength
predictors.
comprises
two
input
variables
combination
(C1
C2)
features
sensitivity
selection.
under-the-receiver
operating
characteristic
curve
(AUC)
root
mean
square
error
(RMSE)
were
utilised
determine
accuracy
a
good
forecast.
validation
findings
showed
that
BE-C1
performed
best
terms
precision,
accuracy,
AUC,
specificity,
as
well
lowest
(RMSE).
performance
skills
overall
models
proved
reliable
with
range
AUC
(89–97%).
can
also
be
beneficial
flash
forecasts
warning
activity
developed
by
disaster
Natural Hazards Research,
Journal Year:
2023,
Volume and Issue:
3(2), P. 247 - 256
Published: Feb. 10, 2023
In
this
study,
flood
susceptibility
mapping
was
carried
out
for
Chemoga
watershed
upper
Abay
River
basin,
Ethiopia.
The
main
objective
of
study
is
to
identify
the
areas
using
Frequency
ratio
and
Information
Values
models.
Based
on
Google
Earth
imagery
filed
survey,
about
168
flooding
locations
were
identified
classified
randomly
into
training
datasets
70%
(118)
remaining
30%
(50)
used
validation
purpose.
Identified
12,
conditioning
factors
such
as
slope,
elevation,
aspect,
curvature,
TWI,
NDVI,
distance
from
road,
river,
soil
texture,
lithology,
land
use
rainfall
integrated
with
determine
weights
each
location
factor
classes
both
frequency
information
value
maps
produced
by
overlay
all
raster
calculator
spatial
analyst
tool
in
ArcGIS
10.4.
final
reclassified
very
low,
moderate,
high
FR
IV
This
validated
area
under
curve
(AUC).
results
AUC
accuracy
models
showed
that
success
rates
82.90%
82.10%,
while
prediction
80.70%
80.00%
respectively.
Past
events
are
compared
vulnerable
database
validate
modeled
output
present
study.
type
will
be
useful
local
government
future
planning
decision
mitigation
plans.
