Overview
- Self-contained book, unique in the literature
- Devoted to the application of neural networks to the concrete problem of time series of sea data, namely significant wave heights and sea levels
- Good reference for a diverse audience of grad students, researchers, and practitioners in neural networks, applied mathematics, data analysis, prediction theory, meteorology, hydraulic, civil and marine engineering
- An excellent introduction to the use of artificial neural networks in an applied math physics/engineering area
- Methods, models and alogrithms developed in the work are useful for the construction of sea structures, ports, and marine experiments
Part of the book series: Modeling and Simulation in Science, Engineering and Technology (MSSET)
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Table of contents (11 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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 networks 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 resultsand novel ideas presented in this useful resource.
Reviews
I strongly encourage both researchers engaged in work on neural networks, data analysis, etc., as well as practitioners to study this very valuable book… --Zentralblatt Math
Those interested in novel applications of neural networks, or machine learning applications in general, may find this book inspiring. – MathSciNet
Authors and Affiliations
Bibliographic Information
Book Title: Neural Networks and Sea Time Series
Book Subtitle: Reconstruction and Extreme-Event Analysis
Authors: Brunello Tirozzi, Silvia Puca, Stefano Pittalis, Antonello Bruschi, Sara Morucci, Enrico Ferraro, Stefano Corsini
Series Title: Modeling and Simulation in Science, Engineering and Technology
DOI: https://doi.org/10.1007/0-8176-4459-8
Publisher: Birkhäuser Boston, MA
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Birkhäuser Boston 2006
Hardcover ISBN: 978-0-8176-4347-8Published: 21 November 2005
eBook ISBN: 978-0-8176-4459-8Published: 12 October 2007
Series ISSN: 2164-3679
Series E-ISSN: 2164-3725
Edition Number: 1
Number of Pages: XII, 180
Number of Illustrations: 64 b/w illustrations
Topics: Geoengineering, Foundations, Hydraulics, Probability Theory and Stochastic Processes, Statistical Theory and Methods, Engineering Fluid Dynamics, Complex Systems, Mathematical Modeling and Industrial Mathematics