Neural Networks and Sea Time Series

[ska51]

Neural Networks and Sea Time Series: Reconstruction and Extreme-Event Analysis

Author: Brunello Tirozzi, Silvia Puca, Stefano Pittalis, Antonello Bruschi, Sara Morucci | Size: 11.31 MB | Format: PDF | Publisher: Birkhauser Boston | Year: 2005 | pages: 180 | ISBN: 0817643478

Increasingly, neural networks are used and implemented in a wide range of fields and have become useful tools in probabilistic analysis and prediction theory. This book—unique in the literature—studies the application of neural networks to the analysis of time series of sea data, namely significant wave heights and sea levels. The particular problem examined as a starting point is the reconstruction of missing data, a general problem that appears in many cases of data analysis.

Specific topics covered include:

* Presentation of general information on the phenomenology of waves and tides, as well as related technical details of various measuring processes used in the study

* Description of the model of wind waves (WAM) used to determine the spectral function of waves and predict the behavior of SWH (significant wave heights); a comparison is made of the reconstruction of SWH time series obtained by means of neural network algorithms versus SWH computed by WAM

* Principles of artificial neural networks, approximation theory, and extreme-value theory necessary to understand the main applications of the book.

* Application of artificial neural networks (ANN) to reconstruct SWH and sea levels (SL)

* Comparison of the ANN approach and the approximation operator approach, displaying the advantages of ANN

* Examination of extreme-event analysis applied to the time series of sea data in specific locations

* Generalizations of ANN to treat analogous problems for other types of phenomena and data

This book, a careful blend of theory and applications, is an excellent introduction to the use of ANN, which may encourage readers to try analogous approaches in other important application areas. Researchers, practitioners, and advanced graduate students in neural networks, hydraulic and marine engineering, prediction theory, and data analysis will benefit from the results and novel ideas presented in this useful resource.

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