Water,
Год журнала:
2024,
Номер
16(20), С. 2944 - 2944
Опубликована: Окт. 16, 2024
Planning,
managing
and
optimising
surface
water
quality
is
a
complex
multifaceted
process,
influenced
by
the
effects
of
both
climate
uncertainties
anthropogenic
activities.
Developing
an
innovative
robust
decision
support
framework
(DSF)
essential
for
effective
efficient
management,
so
it
can
provide
information
on
assist
policy
makers
resource
managers
to
identify
potential
causes
deterioration.
This
crucial
implementing
actions
such
as
infrastructure
development,
legislative
compliance
environmental
initiatives.
Recent
advancements
in
computational
domains
have
created
opportunities
employing
artificial
intelligence
(AI),
advanced
statistics
mathematical
methods
use
improved
management.
study
proposed
comprehensive
conceptual
DSF
minimise
adverse
extreme
weather
events
change
quality.
The
utilises
machine
learning
(ML),
deep
(DL),
geographical
system
(GIS)
statistical
techniques
foundation
this
outcomes
from
our
three
studies,
where
we
examined
application
ML
DL
models
predicting
index
(WQI)
reservoirs,
utilising
find
seasonal
trend
rainfall
quality,
exploring
connection
between
streamflow,
GIS
show
spatial
temporal
variability
hydrological
parameters
WQI.
Three
potable
supply
reservoirs
Toowoomba
region
Australia
were
taken
area
practical
implementation
DSF.
serve
mechanism
distinct
characteristics
understand
correlations
rainfall,
streamflow
will
enable
enhance
their
making
processes
selecting
management
priorities
safeguard
face
future
variability,
including
prolonged
droughts
flooding.
Ecological Indicators,
Год журнала:
2024,
Номер
158, С. 111480 - 111480
Опубликована: Янв. 1, 2024
Planning
for
reducing
climate
changes
impacts
and
human
interventions
on
river
discharge
relies
determining
the
extent
to
which
factor
has
contributed
observed
changes.
However,
diversity
of
approaches
methods
proposed
assessing
impact
climatic
factors,
coupled
with
limitations
in
data
time,
necessitate
careful
selection
suitable
methods.
This
study
aims
address
these
challenges
by
providing
a
reliable
framework
reduce
uncertainty
select
appropriate
quantifying
contribution
factors
runoff
using
available
information.
To
achieve
this
goal,
comparative
analysis
different
was
conducted
three
stages:
trend
determination,
assessment
homogeneity
breakpoints
meteorological
hydrological
watershed,
quantification
followed
validation
against
real
evidence.
Subsequently,
were
employed
evaluate
Boukan
enabling
quantitative
comparison
results.
The
findings
revealed
an
upward
temperature,
downward
precipitation
runoff,
significant
annual
1997.
results
obtained
from
various
exhibited
range
variations,
attributing
35%
85%
reduction
while
accounted
15%
64%.
In
general,
box
plot
indicated
that
approximately
57%
average,
whereas
around
average
47%.
Additionally,
examination
land
use
changes,
resource
utilization,
consumption
patterns
further
supported
notion
playing
more
substantial
role
regional
reduction.
Based
evidence,
double-mass
curve
methods,
water
balance
model,
SWAT
are
identified
as
most
displaying
better
fit
reality.
Therefore,
given
wide
results,
adopting
multiple
methodologies
becomes
crucial
uncertainty.
context,
can
serve
valuable
guide
selecting
decision-making
processes.
Water,
Год журнала:
2024,
Номер
16(20), С. 2944 - 2944
Опубликована: Окт. 16, 2024
Planning,
managing
and
optimising
surface
water
quality
is
a
complex
multifaceted
process,
influenced
by
the
effects
of
both
climate
uncertainties
anthropogenic
activities.
Developing
an
innovative
robust
decision
support
framework
(DSF)
essential
for
effective
efficient
management,
so
it
can
provide
information
on
assist
policy
makers
resource
managers
to
identify
potential
causes
deterioration.
This
crucial
implementing
actions
such
as
infrastructure
development,
legislative
compliance
environmental
initiatives.
Recent
advancements
in
computational
domains
have
created
opportunities
employing
artificial
intelligence
(AI),
advanced
statistics
mathematical
methods
use
improved
management.
study
proposed
comprehensive
conceptual
DSF
minimise
adverse
extreme
weather
events
change
quality.
The
utilises
machine
learning
(ML),
deep
(DL),
geographical
system
(GIS)
statistical
techniques
foundation
this
outcomes
from
our
three
studies,
where
we
examined
application
ML
DL
models
predicting
index
(WQI)
reservoirs,
utilising
find
seasonal
trend
rainfall
quality,
exploring
connection
between
streamflow,
GIS
show
spatial
temporal
variability
hydrological
parameters
WQI.
Three
potable
supply
reservoirs
Toowoomba
region
Australia
were
taken
area
practical
implementation
DSF.
serve
mechanism
distinct
characteristics
understand
correlations
rainfall,
streamflow
will
enable
enhance
their
making
processes
selecting
management
priorities
safeguard
face
future
variability,
including
prolonged
droughts
flooding.
