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Costal Inlets Research Program
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> Fig. 7. RNN--WL model training and verification at Station P8 of Fire Island Inlet.
Default parameters in the RNN--WL model for network training
Model testing in predicting water levels over yearlong periods
Regional_Neural_Network
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W.
Huang
et
al.
/
Ocean
Engineering 30 (2003) 22752295
2286
the
model
weights
and
bias
parameters
can
be
saved
for
future
application.
The
data
set
used
for
model
training
should be continuous
and
without
gaps.
Due to
missing
data
gaps
in
the
Long
Island
field observation
data,
we
were
able
to
use
only
short-
term
continuous
data
sets
for
about
30
days
in
model
training.
Comparison of
model
predictions
and
observations
during
training
and
veri
fication
phases
are
given
in
Fig.
6
for
Station
P2,
and
Fig.
7
for
Station
P8.
Results
show
that
the
RNN--WL
neural
network
model
was
satisfactorily
trained to determine
the
weight
parameters
in
the
network
so
that
the
inputs match
well
with
the
target
time
series.
Moreover,
the
backpropagation
neural
network
was
trained to recognize
the
time
series
pattern.
Keeping
the
same
weight
parameters
determined in
the
training
phase,
the
model
was
able
to
provide
satisfactory predictions
for
an
independent
data
set
during
the
veri
fication
phase.
The
correlation coefficients
between
model
predictions
and
observations
ranged
from
0.968
to
0.985
during
the
veri
fication
phase
for
all
the
stations.
The
root
mean
square
errors
(RMSE)
were
all
approximately
0.06.
A
summary
of
the
statistics of
the
model
performance is
given
in
Table 3
.
As
shown
in
Fig.
7b
,
the
neural
network
model
provides
good
predictions of
water
levels
that
consist of both
tidal
and
non-tidal
signals.
Fig.
7. RNN--WL
model
training
and
veri
fi
cation
at
Station
P8 of
Fire
Island
Inlet.
The
distance
between
input
NOAA
station
at
Montauk
and
the
inlet
is
about
90
km.
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