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