Abstract:
This study discusses site-specific system optimization
efforts related to the capability of a coastal video
station to monitor intertidal topography. The system
consists of two video cameras connected to a PC, and is
operating at the meso-tidal, reflective Faro Beach (Algarve
coast, S. Portugal). Measurements from the period February
4, 2009 to May 30, 2010 are discussed in this study.
Shoreline detection was based on the processing of variance
images, considering pixel intensity thresholds for feature
extraction, provided by a specially trained artificial neural
network (ANN). The obtained shoreline data return rate
was 83%, with an average horizontal cross-shore root mean
square error (RMSE) of 1.06 m. Several empirical
parameterizations and ANN models were tested to estimate
the elevations of shoreline contours, using wave and tidal
data. Using a manually validated shoreline set, the lowest
RMSE (0.18 m) for the vertical elevation was obtained
using an ANN while empirical parameterizations based on
the tidal elevation and wave run-up height resulted in an
RMSE of 0.26 m. These errors were reduced to 0.22 m after
applying 3-D data filtering and interpolation of the
topographic information generated for each tidal cycle.
Average beach-face slope tan(β) RMSE were around 0.02.
Tests for a 5-month period of fully automated operation
applying the ANN model resulted in an optimal, average,
vertical elevation RMSE of 0.22 m, obtained using a one tidal cycle time window and a time-varying beach-face
slope. The findings indicate that the use of an ANN in such
systems has considerable potential, especially for sites
where long-term field data allow efficient training.