Abstract
In this study, we investigate the potential of using a novel neural-fuzzy network to predict the coastal erosion from bathymetry field data taken from the Eresos beach located at the SW coastline of Lesvos island, Greece. The bathymetry data were collected using specialized experimental devices deployed in the study area. To elaborate the data and predict the coastal erosion, we have developed a neural-fuzzy network implemented in three phases. The first phase defines the rule antecedent parts and includes three layers of hidden nodes. The second phase employs truncated Hermite polynomial series to form the rule consequent parts. Finally, the third phase intertwines the information coming from the above phases and infers the network’s output. The performance of the network is compared to other two relative approaches. The simulation study shows that the network achieves an accurate behavior, while outperforming the other methods.
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Tsekouras, G.E., Rigos, A., Chatzipavlis, A., Velegrakis, A. (2015). A Neural-Fuzzy Network Based on Hermite Polynomials to Predict the Coastal Erosion. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2015. Communications in Computer and Information Science, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-319-23983-5_19
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DOI: https://doi.org/10.1007/978-3-319-23983-5_19
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