Water,
Год журнала:
2024,
Номер
16(24), С. 3656 - 3656
Опубликована: Дек. 18, 2024
In
recent
years,
frequent
floods
caused
by
heavy
rainfall
and
persistent
precipitation
have
greatly
affected
changes
in
groundwater
levels.
This
has
not
only
huge
economic
losses
human
casualties,
but
also
had
a
significant
impact
on
the
ecological
environment.
The
aim
of
this
study
is
to
explore
effectiveness
new
method
based
Long
Short-Term
Memory
networks
(LSTM)
its
optimization
model
level
prediction
compared
with
traditional
method,
evaluate
accuracy
different
models,
identify
main
factors
affecting
level.
Taking
Chaoyang
City
Liaoning
Province
as
an
example,
four
assessment
indicators,
R2,
MAE,
RMSE,
MAPE,
were
used.
results
show
that
optimized
LSTM
outperforms
both
underlying
all
metrics,
GWO-LSTM
performing
best.
It
was
found
high
water-table
anomalies
are
mainly
or
storms.
Changes
water
table
can
negatively
affect
environment
such
vegetation
growth,
soil
salinization,
geological
hazards.
accurate
levels
scientific
importance
for
development
sustainable
cities
communities,
well
good
health
well-being
beings.
Advances in environmental engineering and green technologies book series,
Год журнала:
2024,
Номер
unknown, С. 175 - 202
Опубликована: Дек. 6, 2024
Groundwater
is
a
natural
renewable
resource
vital
for
any
life
on
Earth.
management
of
emerging
concern
the
conservation
and
protection
this
resource.
With
advent
innovative
technologies,
managing
such
resources
become
easier
to
some
extent.
This
chapter
illustrates
advanced
their
contribution,
challenges
future
prospects
sustainable
groundwater.
AI
methods
have
widespread
in
decision-making
recent
years
are
accepted
globally
due
cost-effectiveness,
time-saving,
efficient
nature.
AI-driven
models
provide
precise
analytical
modelling,
real-time
monitoring,
data
integration
groundwater
management.
Innovative
can
detect
vulnerable
regions
that
prone
pollution
depletion
level
draw
attention
scientists,
local
people
policymakers
prompt
intervention.
Water,
Год журнала:
2024,
Номер
16(24), С. 3656 - 3656
Опубликована: Дек. 18, 2024
In
recent
years,
frequent
floods
caused
by
heavy
rainfall
and
persistent
precipitation
have
greatly
affected
changes
in
groundwater
levels.
This
has
not
only
huge
economic
losses
human
casualties,
but
also
had
a
significant
impact
on
the
ecological
environment.
The
aim
of
this
study
is
to
explore
effectiveness
new
method
based
Long
Short-Term
Memory
networks
(LSTM)
its
optimization
model
level
prediction
compared
with
traditional
method,
evaluate
accuracy
different
models,
identify
main
factors
affecting
level.
Taking
Chaoyang
City
Liaoning
Province
as
an
example,
four
assessment
indicators,
R2,
MAE,
RMSE,
MAPE,
were
used.
results
show
that
optimized
LSTM
outperforms
both
underlying
all
metrics,
GWO-LSTM
performing
best.
It
was
found
high
water-table
anomalies
are
mainly
or
storms.
Changes
water
table
can
negatively
affect
environment
such
vegetation
growth,
soil
salinization,
geological
hazards.
accurate
levels
scientific
importance
for
development
sustainable
cities
communities,
well
good
health
well-being
beings.
