Neuro-Fuzzy Network for Modeling the Shoreline Realignment of the Kamari Beach, Santorini, Greece

In this paper, a novel multiple-layer neuro-fuzzy network is proposed to model/predict shoreline realignment at a highly touristic island beach (Kamari beach, Santorini, Greece). A specialized experimental setup was deployed to generate a set of input-output data that comprise parameters describing the beach morphology and wave conditions and the cross-shore shoreline position at 30 cross-sections of the beach extracted from coastal video imagery, respectively. The proposed network consists of three distinct modules. The first module concerns the network representation of a fuzzy model equipped with a typical inference mechanism. The second module implements a novel competitive learning network to generate initial values for the rule base antecedent parameters. These parameters are, then, used to facilitate the third module that employs particle swarm optimization to perform a stochastic search for optimal parameter estimation. The network is compared favorably to two other neural networks: a radial basis function neural network and a feedforward neural network. Regarding the effectiveness of the proposed network to model shoreline re-alignment, the RMSE found (7.2–7.7 m, depending on the number of rules/nodes), reflects the high variability of the shoreline position of the Kamari beach during the period of observations: the RMSE is of a similar order to the standard deviation (up to 8 m) of the cross-shore shoreline position. The results are encouraging and the effectiveness of the proposed network could be further improved by changes (fine-tuning) of the input variables.