Neural Networks and Sea Time Series Reconstruction and Extreme-Event Analysis [Hardcover]

Devoted to the application of neural networks to the concrete problem of time series of sea data

Good reference for a diverse audience of grad students, researchers, and practitioners in applied mathematics, data analysis, meteorlogy, hydraulic, civil and marine engineering

Methods, models and alogrithms developed in the work are useful for the construction of sea structures, ports, and marine experiments

 

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 bookunique in the literaturestudies 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 oflc"