The Reconstruction of Significant Wave Height Time Series by Using a Neural Network Approach

Author:

Arena Felice1,Puca Silvia2

Affiliation:

1. Department of Mechanics and Materials–University ‘Mediterranea’ of Reggio Calabria, Loc. Feo di Vito–89100 Reggio Calabria–Italy

2. Department of Physics–University of Rome ‘La Sapienza’, Piazzale Aldo Moro 2–I-00185 Roma–Italy

Abstract

A Multivariate Neural Network (MNN) algorithm is proposed for the reconstruction of significant wave height time series, without any increase of the error of the MNN output with the number of modelled data. The algorithm uses a weighted error function during the learning phase, to improve the modelling of the higher significant wave height. The ability of the MNN to reconstruct sea storms is tested by applying the equivalent triangular storm model. Finally an application to the NOAA buoys moored off California shows a good performance of the MNN algorithm, both during sea storms and calm time periods.

Publisher

ASME International

Subject

Mechanical Engineering,Ocean Engineering

Reference18 articles.

1. Puca, S., and Tirozzi, B., 2003, “A Neural Algorithm for the Reconstruction of Space-Time Correlated Series,” Seminarberichte, Fachbereich Mathematik, Hagen, 74, pp. 81–89.

2. Hidalgo, O., Nieto, J. C., Cunha, C., and Guedes Soares, C., 1995, “Filling Missing Observations in Time Series of Significant Wave Height,” Proc., 14th International Conference on Offshore Mechanics & Arctic Engineering (OMAE’95), Copenhagen, Vol. II, ASME, pp. 9–18.

3. Soares, C. Guedes, and Cunha, C., 2000, “Bivariate Autoregressive Models for the Time Series of Significant Wave Height and Mean Period,” Coastal Eng., 40, pp. 297–311.

4. Boccotti, P., 1986, “On coastal and offshore structure risk analysis.” Excerpta of the Italian Contribution to the Field of Hydraulic Engineering, 1, pp. 19–36.

5. Boccotti, P., 2000, Wave Mechanics for Ocean Engineering, Elsevier Science, Oxford, pp. 1–496.

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