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2. Review of artificial neural network applications
Artificial neural networks (ANN) have proven their usefulness in a number of
research areas such as electronics, aerospace, and manufacturing engineering (Hagan
through nodes or neurons. It is capable of directly correlating input time series of
forcing functions to the output variables through the interconnected nodes with train-
able weights and bias. In contrast to traditional harmonic analysis (Ippen, 1966),
which is used only in the predictions of periodic tidal component, the neural network
model can be trained to recognize and predict both nonlinear and non-periodic sig-
nals. Wong and Wilson's (1984) study of 30-day data indicates that sub-tidal sea
level variation plays an important role in the column exchange between estuary and
ocean through the inlets in the Long Island South Shore. The traditional harmonic
analysis method is unable to provide accurate predictions of long-term water level
variations along Long Island where non-tidal sea level variation is significant.
In applying a trained and validated ANN, output variables are directly calculated
without iteration from the input variables and the vectors of weights and bias in the
network nodes. This functioning is similar to directly find the output from a linear
regression function. Therefore, applying an ANN model takes much less compu-
tational time than the traditional fluid mechanic models, as long as data is available
to establish the ANN model. For this reason, some researchers have combined fluid
mechanics modeling with neural networks to improve the efficiency of model appli-
cations (Bibike and Abbot, 1999). Greenman and Roth at the NASA Ames Research
Center incorporated neural networks with finite element fluid mechanics models to
optimize airfoil design (Greenman and Roth, 1999). A fluid mechanics model can
be used to provide time series outputs of system responses under a few study scen-
arios for a period of time. The time series outputs from fluid mechanics model simul-
ations and forcing functions can then be used as "data" for neural network model
development. A validated neural network model can serve as a cost-effective tool
in quickly assessing the system response to the input factors.
The application of neural networks in oceanographic study is relatively new due
mainly to highly nonlinear characteristics. Hsieh and Tang (1998) discussed several
typical obstacles and provided some suggestions to incorporate neural networks with
other time series forecasting approaches. There are some successful ANN appli-
cations in coastal engineering. For example, Bibike et al. (1999) used ANN to encap-
sulate numerical hydrodynamic model simulations for cost-effective forecasting of
water levels. Mase et al. (1995) adopted ANN to assess the stability of armor unit
and rubble-mound breakwater and found satisfactory agreement between obser-
vations and model predictions. Huang and Foo (2000) employed neural networks to
directly correlate time series of salinity to the forcing functions of winds, water
levels, and freshwater inputs in a multiple-inlet estuary of Apalachicola Bay, Florida.
toric data at the same station, which did not address the non-periodic sub-tidal sea
levels and the correlation with tidal data at other stations. Tsai et al's (2002) ANN
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