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- Beach erosion and recovery during consecutive storms at a steep-sloping, meso-tidal beachPublication . Vousdoukas, Michalis; Almeida, Luis Pedro; Ferreira, ÓscarThis study analyses beach morphological change during six consecutive storms acting on the meso-tidal Faro Beach (south Portugal) between 15 December 2009 and 7 January 2010. Morphological change of the sub-aerial beach profile was monitored through frequent topographic surveys across 11 transects. Measurements of the surf/swash zone dimensions, nearshore bar dynamics, and wave run-up were extracted from time averaged and timestack coastal images, and wave and tidal data were obtained from offshore stations. All the information combined suggests that during consecutive storm events, the antecedent morphological state can initially be the dominant controlling factor of beach response; while the hydrodynamic forcing, and especially the tide and surge levels, become more important during the later stages of a storm period. The dataset also reveals the dynamic nature of steep-sloping beaches, since sub-aerial beach volume reductions up to 30m3/m were followed by intertidal area recovery (–2
- Performance of intertidal topography video monitoring of a meso-tidal reflective beach in South PortugalPublication . Vousdoukas, Michalis; Ferreira, P. M.; Almeida, Luis Pedro; Dodet, Guillaume; Psaros, Fotis; Andriolo, Umberto; Taborda, Rui; Silva, Ana Nobre; Ruano, Antonio; Ferreira, ÓscarThis 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.
- Correlating wave hindcast and buoy data with artificial neural networksPublication . Almeida, Luis Pedro; Vousdoukas, Michalis; Ferreira, P. M.; Ruano, AntonioThis work presents results from the use of Artificial Neural Networks (ANN) to improve wave models hindcasting capacity off the South coast of Portugal. Comparison of the original model results with field measurements showed significant non linear deviations. To compensate for such deviations, a three-layer Multilayer Perceptron (MLP – a type of an ANN) was trained, using the Levenberg-Marquardt method, to improve the fit between the hindcast (generated by WW3) and Faro buoy data in an effort to reconstruct missing data from the wave buoy time series. The results obtained so far are very positive; with the training with annual datasets showing better results than the training with the entire dataset, while both improved significantly the fitting of the raw model results. Further improvements are expected by trying different ANN types, by searching for optimised ANN input-output structure, and by performing sub-set selection on the data sets.