Journal of Hydroinformatics,
Journal Year:
2024,
Volume and Issue:
26(11), P. 2928 - 2938
Published: Nov. 1, 2024
ABSTRACT
Flooding
in
remote
regions
presents
significant
challenges
due
to
data
scarcity,
complicating
impact
assessment
and
mitigation
efforts.
This
research
delineates
an
integrated
methodology
for
quantifying
flood
impacts
such
contexts.
By
leveraging
machine-learning
algorithms,
Sentinel-1
synthetic
aperture
radar
(SAR)
imagery
was
combined
with
digital
elevation
model
river
proximity
metrics
predict
accurately
demarcate
extents.
Geographic
information
systems
overlay
techniques
were
then
employed
spatial
analysis
of
the
floods’
on
population
infrastructural
assets.
The
applied
a
case
study
Ngabang
District,
Indonesia,
demonstrating
its
utility.
Analysis
using
decision
tree,
random
forest
(RF),
gradient
boosting
machine
models
provided
critical
insights
into
prediction
factors.
RF
chosen
as
best,
successfully
identified
flood-prone
regions,
achieving
accuracy
0.94
Kappa
0.87
testing
data,
robustness.
map
showed
impacts,
affecting
373.81
hectares,
10,706
people,
1,500
buildings,
15
km
roads.
highlights
importance
proximity,
elevation,
SAR
imagery,
iterative
improvements
prediction,
offering
valuable
management
efforts
data-scarce
regions.
The Science of The Total Environment,
Journal Year:
2025,
Volume and Issue:
975, P. 179181 - 179181
Published: April 7, 2025
The
integration
of
satellite-based
observations
into
hydrological
models
contributes
to
achieving
more
precise
simulations,
thus
supporting
hazard
mitigation
and
policy-making
especially
in
poorly
gauged
basins.
Sub-monthly
Terrestrial
Water
Storage
(TWS)
derived
from
the
Gravity
Recovery
Climate
Experiment
(GRACE)
mission
have
been
shown
contain
useful
information
for
prediction
monitoring
sub-monthly
water
storage
anomalies
such
as
floods.
This
study
assesses,
first
time,
benefits
challenges
integrating
TWS
a
large-scale
model
during
flood
events.
experiment
is
carried
out
Brahmaputra
River
Basin
performed
through
state-of-the-art
sequential
Data
Assimilation
(DA)
with
aim
improving
estimates.
results
indicate
that
daily
DA,
based
on
Ensemble
Kalman
Filter
(EnKF),
successfully
introduces
observed
variability
(differences
below
10
mm
GRACE
TWS).
DA
spatially
vertically
downscales
updates
timing
distribution.
Especially,
it
modifies
river
compartment,
where
variations
are
expected
In
contrast,
monthly
implemented
both
an
EnKF
Smoother
(EnKS),
introduce
undesired
peaks
time
series.
Choosing
adequate
covariance
localization
found
be
crucial
DA.
Finally,
statistical
characteristics
translation
discharge
investigated,
recommendations
future
developments
provided.
Journal of Hydroinformatics,
Journal Year:
2024,
Volume and Issue:
26(9), P. 2389 - 2415
Published: Aug. 13, 2024
ABSTRACT
Floods
threaten
the
environment
and
human
settlements
across
river
basins
globally,
including
Upper
Krishna
Basin
in
India.
This
research
delves
into
evaluating
flood
hazard
areas
within
utilizing
Analytical
Hierarchy
Process
(AHP),
Frequency
Ratio
(FR),
Statistical
Index
(SI).
These
methodologies
prioritize
classify
flood-prone
regions
by
integrating
spatial
non-spatial
criteria.
The
findings
reveal
significant
variations
risk
classification
based
on
three
models.
AHP
model
identifies
3.37%
of
region
as
low
risk,
with
22.90%
classified
moderate
68.27%
high
risk.
In
contrast,
FR
designates
3.76%
10.50%
42.21%
Meanwhile,
SI
1.04%
35.38%
under-high
57.87%
very
Validation
using
Receiver
Operating
Characteristic-Area
Under
Curve
(ROC-AUC)
values
demonstrates
superior
reliability
model.
offer
valuable
insights
for
decision-makers
to
allocate
resources
implement
effective
mitigation
measures.
