International Journal of Applied Earth Observation and Geoinformation,
Journal Year:
2023,
Volume and Issue:
116, P. 103177 - 103177
Published: Jan. 3, 2023
Despite
satellite-based
precipitation
products
(SPPs)
providing
a
worldwide
span
with
high
spatial
and
temporal
resolution,
their
efficiency
in
disaster
risk
forecasting,
hydrological,
watershed
management
remains
challenge
due
to
the
significant
dependence
of
rainfall
on
spatiotemporal
pattern
geographical
features
each
area.
This
research
proposes
an
effective
deep
learning-based
solution
that
combines
convolutional
neural
network
benefit
encoder-decoder
architecture
eliminate
pixel-by-pixel
bias
enhance
accuracy
daily
SPPs.
work
uses
five
gridded
products,
four
which
are
(TRMM,
CMORPH,
CHIRPS,
PERSIANN-CDR)
one
is
gauge-based
(APHRODITE).
The
Lancang-Mekong
River
Basin
(LMRB),
international
basin,
was
chosen
as
region
because
its
diverse
climate
spread
spanning
six
countries.
According
results
analyses,
TRMM
product
exhibits
better
performance
than
other
three
learning
model
proved
efficacy
by
successfully
reducing
spatial–temporal
gap
between
SPPs
APHRODITE.
In
addition,
ADJ-TRMM
performed
best
corrected
items,
followed
ADJ-CDR
ADJ-CHIRPS
products.
study's
findings
indicate
SPP
has
advantages
disadvantages
across
LMRB.
aftermath
discontinuation
APHRODITE
2015,
we
believe
framework
will
be
for
generating
more
up-to-date
dependable
dataset
LMRB
research.
Journal of Cleaner Production,
Journal Year:
2024,
Volume and Issue:
441, P. 140715 - 140715
Published: Jan. 11, 2024
Water
is
the
most
valuable
natural
resource
on
earth
that
plays
a
critical
role
in
socio-economic
development
of
humans
worldwide.
used
for
various
purposes,
including,
but
not
limited
to,
drinking,
recreation,
irrigation,
and
hydropower
production.
The
expected
population
growth
at
global
scale,
coupled
with
predicted
climate
change-induced
impacts,
warrants
need
proactive
effective
management
water
resources.
Over
recent
decades,
machine
learning
tools
have
been
widely
applied
to
resources
management-related
fields
often
shown
promising
results.
Despite
publication
several
review
articles
applications
water-related
fields,
this
paper
presents
first
time
comprehensive
techniques
management,
focusing
achievements.
study
examines
potential
advanced
improve
decision
support
systems
sectors
within
realm
which
includes
groundwater
streamflow
forecasting,
distribution
systems,
quality
wastewater
treatment,
demand
consumption,
marine
energy,
drainage
flood
defence.
This
provides
an
overview
state-of-the-art
approaches
industry
how
they
can
be
ensure
supply
sustainability,
quality,
drought
mitigation.
covers
related
studies
provide
snapshot
industry.
Overall,
LSTM
networks
proven
exhibit
reliable
performance,
outperforming
ANN
models,
traditional
established
physics-based
models.
Hybrid
ML
exhibited
great
forecasting
accuracy
across
all
showing
superior
computational
power
over
ANNs
architectures.
In
addition
purely
data-driven
physical-based
hybrid
models
also
developed
prediction
performance.
These
efforts
further
demonstrate
Machine
powerful
practical
tool
management.
It
insights,
predictions,
optimisation
capabilities
help
enhance
sustainable
use
development,
healthy
ecosystems
human
existence.
Journal of Hydrology,
Journal Year:
2023,
Volume and Issue:
627, P. 130380 - 130380
Published: Oct. 21, 2023
Streamflow
forecasting
is
crucial
in
water
planning
and
management.
Physically-based
hydrological
models
have
been
used
for
a
long
time
these
fields,
but
improving
forecast
quality
still
an
active
area
of
research.
Recently,
some
artificial
neural
networks
found
to
be
effective
simulating
predicting
short-term
streamflow.
In
this
study,
we
examine
the
reliability
Long
Short-Term
Memory
(LSTM)
deep
learning
model
streamflow
lead
times
up
ten
days
over
Canadian
catchment.
The
performance
LSTM
compared
that
process-based
distributed
model,
with
both
using
same
weather
ensemble
forecasts.
Furthermore,
LSTM’s
ability
integrate
observed
on
issue
date
data
assimilation
process
required
reduce
initial
state
biases.
Results
indicate
forecasted
streamflows
are
more
reliable
accurate
lead-times
7
9
days,
respectively.
Additionally,
it
shown
recent
flows
as
predictor
can
smaller
errors
first
without
requiring
explicit
step,
generating
median
value
mean
absolute
error
(MAE)
day
lead-time
across
all
dates
25
m3/s
115
assimilated
model.
Results in Engineering,
Journal Year:
2024,
Volume and Issue:
22, P. 102215 - 102215
Published: May 4, 2024
The
Narmada
River
basin,
a
significant
water
resource
in
central
India,
plays
crucial
role
supporting
agricultural,
industrial,
and
domestic
supply.
Effective
management
of
this
basin
requires
accurate
streamflow
forecasting,
which
has
become
increasingly
important.
This
study
delves
into
forecasting
using
historical
data
from
five
major
river
stations,
covering
the
upper
reaches
East
middle
sections.
dataset
spans
1978
to
2020
undergoes
rigorous
screening
preparation,
including
normalization
StandardScaler.
research
adopts
comprehensive
approach,
developing
models
for
training
on
70%
data,
validation
most
current
15%,
testing
against
future
targets
with
another
15%
data.
To
achieve
precise
predictions,
suite
machine
learning
is
employed,
CatBoost,
LGBM
(Light
Gradient
Boosting
Machine),
Random
Forest,
XGBoost.
Performance
evaluation
these
relies
key
indices
such
as
mean
squared
error
(MSE),
absolute
(MAE),
root
square
(RMSE),
percent
(RMSPE),
normalized
(NRMSE),
R-squared
(R2).
Notably,
among
models,
Forest
emerges
robust
prediction,
showcasing
its
effectiveness
handling
complexities
hydrological
forecasting.
contributes
significantly
field
by
providing
insights
performance
various
models.
findings
not
only
enhance
our
understanding
watershed
dynamics
but
also
highlight
pivotal
that
can
play
improving
sustainable
management.
