Buch, Englisch, 180 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 459 g
Reihe: Modeling and Simulation in Science, Engineering and Technology
Reconstruction and Extreme-Event Analysis
Buch, Englisch, 180 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 459 g
Reihe: Modeling and Simulation in Science, Engineering and Technology
ISBN: 978-0-8176-4347-8
Verlag: Birkhäuser Boston
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik
- Naturwissenschaften Physik Mechanik Kontinuumsmechanik, Strömungslehre
- Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Strömungslehre
- Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
- Geowissenschaften Umweltwissenschaften Wasserversorgung
- Technische Wissenschaften Bauingenieurwesen Wasserbau
- Technische Wissenschaften Umwelttechnik | Umwelttechnologie Abfallwirtschaft, Abfallentsorgung
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Fuzzy-Systeme
- Rechtswissenschaften Öffentliches Recht Umweltrecht Abfall- und Bodenschutzrecht
- Geowissenschaften Geologie Marine Geologie, Ozeanographie (Meereskunde)
- Geowissenschaften Umweltwissenschaften Abfallbeseitigung, Abfallentsorgung
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Geowissenschaften Umweltwissenschaften Umweltverschmutzung, Umweltkriminalität, Umweltrecht
- Naturwissenschaften Physik Angewandte Physik Statistische Physik, Dynamische Systeme
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
Weitere Infos & Material
Basic Notions on Waves and Tides.- The Wave Amplitude Model.- Artificial Neural Networks.- Approximation Theory.- Extreme-Value Theory.- Application of ANN to Sea Time Series.- Application of Approximation Theory and ARIMA Models.- Extreme-Event Analysis.- Generalization to Other Phenomena.- Conclusions.
