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A neural network for the prediction of wave reflection from coastal and harbor structures
Zanuttigh, B.; Formentin, S.M.; Briganti, R. (2013). A neural network for the prediction of wave reflection from coastal and harbor structures. Coast. Eng. 80: 49-67. dx.doi.org/10.1016/j.coastaleng.2013.05.004
In: Coastal Engineering: An International Journal for Coastal, Harbour and Offshore Engineers. Elsevier: Amsterdam; Lausanne; New York; Oxford; Shannon; Tokyo. ISSN 0378-3839, more
Peer reviewed article  

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Keyword
    Marine
Author keywords
    Wave reflection; Artificial neural network; Database; Coastal structures; Empirical formulae; Sensitivity analysis

Project Top | Authors 
  • Innovative coastal technologies for safer European coasts in a changing climate, more

Authors  Top 
  • Zanuttigh, B.
  • Formentin, S.M.
  • Briganti, R.

Abstract
    This paper presents the development of an Artificial Neural Network for the prediction of the wave reflection coefficient from a wide range of coastal and harbor structures. The Artificial Neural Network is trained and validated against an extensive database of about 6000 data, including smooth, rock and armor unit slopes, berm breakwaters, vertical walls, low crested structures, oblique wave attacks. The structure and data included in this database, as well as the approach used in this paper, follow the work done on wave overtopping within the CLASH project.In this new Artificial Neural Network 13 input elements are used to represent the physics of the reflection process taking into account the structure geometry (height, submergence, straight or non-straight slope, with or without berm or toe), the structure type (smooth or covered by an armor layer, with permeable or impermeable core) and the wave attack (water depth, wave height, wave length, wave obliquity, directional spreading).The selection of the input elements and of the algorithms used in the network is described based on an in-depth sensitivity analysis of the network performance.The accuracy of the network is quite satisfactory, being the average root mean squared error lower than 0.04. This value is consistent between the Artificial Neural Network calibrated on the original dataset and the one calibrated on boot-strapped datasets in which data reliability and structure complexity are considered.The performance of the network is compared for limited datasets with selected available literature formulae proving that this approach is able to estimate the experimental reflection coefficients with greater accuracy than the empirical formulae calibrated on these same datasets.

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