Water,
Journal Year:
2020,
Volume and Issue:
12(10), P. 2714 - 2714
Published: Sept. 28, 2020
Green
infrastructure
(GI)
is
a
contemporary
area
of
research
worldwide,
with
the
implementation
findings
alleviating
issues
globally.
As
supplement
and
alternative
to
gray
infrastructure,
GI
has
multiple
integrated
benefits.
Multi-objective
optimization
seeks
provide
maximum
The
purpose
this
review
highlight
multifunctional
effectiveness
summarize
its
multi-objective
methodology.
Here,
in
hydrology,
energy,
climate,
environment,
ecology,
humanities
as
well
their
interrelationships
are
summarized.
Then,
main
components
including
spatial
scale
application,
objectives,
decision
variables,
methods
procedure
relationships
mathematical
representation
examined.
However,
certain
challenges
still
exist.
There
no
consensus
on
how
measure
optimize
multi-functional
GI.
Future
directions
such
enhancing
assessment
optimization,
improving
life
cycle
analysis
cost,
integrating
benefits
based
future
uncertainties
developing
green–gray
discussed.
This
vital
for
final
decision-making
stakeholders.
The Journal of Open Source Software,
Journal Year:
2020,
Volume and Issue:
5(52), P. 2292 - 2292
Published: Aug. 4, 2020
Stormwater
management
seeks
to
reduce
runoff
from
rain
or
melted
snow
and
improve
water
quality.Where
it
can
absorb
into
soil,
is
filtered
returns
streams,
rivers,
aquifers,
but
in
developed
areas,
precipitation
often
cannot
soak
the
ground
because
impervious
surfaces
(e.g.,
pavement,
buildings),
already
saturated
soils
create
excess
runoff.This
water,
which
contain
pollutants,
then
runs
across
urban
storm
drains,
drainage
ditches,
sewer
systems.Stormwater
cause
flooding,
erosion,
infrastructure
habitat
damage,
contamination
(including
combined
sanitary
overflows).In
effective
stormwater
that
routes
detains
helps
mitigate
these
impacts
quality.
The Science of The Total Environment,
Journal Year:
2021,
Volume and Issue:
806, P. 150447 - 150447
Published: Sept. 27, 2021
Decision
Support
Systems
(DSS)
for
Sustainable
Urban
Drainage
(SUDS)
are
a
valuable
aid
SUDS
widespread
adoption.
These
tools
systematize
the
decision-making
criteria
and
eliminate
bias
inherent
to
expert
judgment,
abridging
technical
aspect
of
non-technical
users
decision-makers.
Through
collection
careful
assessment
120
papers
on
models
SUDS-DSS,
this
review
shows
how
these
built,
selected,
used
assist
decision-makers
questions.
The
manuscript
classifies
DSS
based
question
they
in
answering,
spatial
scale
used,
software
among
other
aspects.
SUDS-DSS
aspects
that
require
more
attention
identified,
including
environmental
social
considerations,
trains
performance
selection,
stochasticity
rainfall,
future
scenarios
impact.
Suggestions
finally
offered
better
equip
facing
emerging
stormwater
challenges
urban
centers.
Abstract
Urban
flooding
is
a
key
global
challenge
which
expected
to
become
exacerbated
under
change
due
more
intense
rainfall
and
flashier
runoff
regimes
over
increasingly
urban
landscapes.
Consequently,
many
cities
are
rethinking
their
approach
flood
risk
management
by
using
green
infrastructure
(GI)
solutions
reverse
the
legacy
of
hard
engineering
approaches.
The
aim
GI
attenuate,
restore,
recreate
natural
response,
bringing
hydrological
responses
closer
pre‐urbanized
conditions.
However,
effectiveness
often
difficult
determine,
depends
on
both
magnitude
storm
events
spatial
scale
infrastructure.
Monitoring
successes
failures
schemes
not
routinely
conducted.
Thus,
it
can
be
determine
whether
provides
sustainable
solution
manage
flooding.
This
article
an
international
perspective
current
use
for
mitigation
offers
in
light
future
challenges.
An
increasing
body
literature
further
suggests
that
optimized
alongside
gray
provide
holistic
delivers
multiple
co‐benefits
environment
society,
while
resilience.
will
have
work
synergistically
with
existing
upgraded
if
managed
futureproof
manner.
Here,
we
discuss
series
priorities
challenges
must
overcome
enable
integration
into
stormwater
frameworks
effectively
risk.
categorized
under:
Engineering
Water
>
Sustainable
Planning
Science
Extremes
