A neural network for the prediction of wave reflection from coastal and harbor structures

Zanuttigh, Barbara; Formentin, Sara Mizar; Briganti, Riccardo

doi:10.1016/j.coastaleng.2013.05.004

Cited by 41 publications

(17 citation statements)

References 51 publications

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“…Moreover, an additional geometric parameter has been introduced: the average unit size D representative of the structure elements, which has proved to be relevant in the prediction of K r and K t (Panizzo and Briganti, 2007;Formentin and Zanuttigh, 2013;Zanuttigh et al, 2013). Therefore, the complete database, in its final layout, consists of 13 hydraulic parameters, 18 structural parameters and 3 general parameters (the reliability and complexity factors and the identify name of the test).…”

Section: The Databasementioning

confidence: 99%

“…The velocity and the efficiency of the training depend on the transfer functions, the error type to minimize, the tolerance imposed and on the training algorithm. The initial ANN architecture has been directly derived from ANN (2), based on the analysis presented already by Zanuttigh et al (2013) and Formentin and Zanuttigh (2013), and then tested against the prediction of all the three processes. The resulting optimal characteristics of the ANN architecture are resumed in the following:  multilayer network, based on a "feed-forward back-propagation" learning algorithm; 1 hidden layer, and 1 output layer, corresponding either to K r , K t or q.…”

Section: The Ann Architecturementioning

confidence: 99%

“…In order to define the most suitable set of input parameters and the best architecture for the ANN, two of the existing ANNs have been analysed, compared and modified:  the ANN for wave overtopping developed within the CLASH project by Van Gent (2007), referred in the following as ANN (1);  the ANN for reflection proposed by Zanuttigh et al (2013), named ANN (2).…”

Section: The Input Parametersmentioning

confidence: 99%

“…To achieve this goal, two of the existing ANNs have been analysed, tested and modified, specifically the original CLASH ANN (Van Gent et al, 2007) and the ANN for reflection proposed by Zanuttigh et al (2013). Once selected the best input set, a sensitivity 1 DICAM, Università di Bologna, Viale Risorgimento 2, Bologna, 40136, Italy 2 Van der Meer Consulting bv, P.O.…”

Section: Introductionmentioning

confidence: 99%

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Advances in Modelling Wave-Structure Interaction Through Artificial Neural Networks

Zanuttigh¹,

Formentin²,

Meer³

2014

Int. Conf. Coastal. Eng.

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This contribution describes a new Artificial Neural Network (ANN) able to predict at once the main parameters representative of the wave-structure interaction processes, i.e. the wave overtopping discharge, the wave transmission coefficient and the wave reflection coefficient. The development of this ANN started with the preparation of a new extended and homogeneous database (derived from CLASH database) which collects all the available tests including at least one of the three parameters, for a total amount of 16,165 data. Some of the existing ANNs were compared and improved, leading to the selection of a final ANN, whose architecture was optimized through an in-depth sensitivity analysis to the learning and training ANN features. The new ANN here proposed provides accurate predictions for all the three parameters, resulting in a tool that can be efficiently used for design purposes. Keywords: artificial neural network, database, wave overtopping, wave reflection, wave transmission INTRODUCTIONThe design of coastal and harbour structures requires a systematic analysis of all the processes of wave-structure interaction, which takes into account the combined effects of wave overtopping, wave transmission and wave reflection. Indeed, all these phenomena should be considered as different outcomes of the same physical process, and therefore should be investigated contemporarily. However, based on the traditional approach most of the existing formulae are targeted to represent just one process and are fitted on specific (more or less wide) databases, addressing usually one or few structure types and having therefore a specific (more or less narrow) validity field.The development and/or use of an Artificial Neural Network (ANN) is therefore particularly recommended in case of complicated structure geometries and variable wave conditions. This kind of predictive method requires however a homogeneous and "wide-enough" database: the number of data should be sufficient for training the ANN based on a number of ANN parameters and including a sufficiently wide number of data for all range of possible output values. There are specific cases that also an ANN cannot deal with, such as very complex walls, perforated caissons and double promenades, see for details EurOtop (2007) and specifically the methodology released within the PC-OVERTOPPING calculator (http://www.overtopping-manual.com/calculation_tool.html).One of the most successful ANNs is the method available from CLASH (2004) and EurOtop (2007) for wave overtopping, which was specifically developed to predict the overtopping discharge for a wide range of coastal structures, including complex geometries (Van Gent et al., 2007). Each of these ANNs actually proved to be able to overcome some of the limits imposed by the traditional empirical formulae, but they are still restricted to reproduce only one of the processes involved in the wave-structure interaction. Nevertheless, the assumption that all the processes are physically correlated implies that a unique set of phys...

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Section: The Databasementioning

confidence: 99%

Section: The Ann Architecturementioning

confidence: 99%

Section: The Input Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Advances in Modelling Wave-Structure Interaction Through Artificial Neural Networks

Zanuttigh¹,

Formentin²,

Meer³

2014

Int. Conf. Coastal. Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Prediction of sea level time series (sea level for short) is desired in geosciences and ocean engineering [21][22][23], for many purposes such as understanding fluctuations of sea level rise, its dynamics and so forth [24][25][26][27][28][29][30][31]. Various methods and technologies have been reported for time series prediction, such as linear predictors [32][33][34][35][36][37][38][39][40][41], nonlinear predictions [42][43][44][45][46], and those based on artificial intelligence, including fuzzy systems [47][48][49][50][51] and artificial neural networks (ANN) [52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67]…”

Section: Introductionmentioning

confidence: 99%