Michael Siek

Predicting Storm Surges: Chaos, Computational Intelligence, Data Assimilation and Ensembles (eBook, PDF)

UNESCO-IHE PhD Thesis

• Format: PDF

Produktbeschreibung

Accurate predictions of storm surge are of importance in coastal areas. This book focuses on data-driven modelling using methods of nonlinear dynamics and chaos theory for predicting storm surges. A number of new enhancements are presented: phase space dimensionality reductionincomplete time seriesphase error correctionfinding true neighboursoptimization of chaotic modeldata assimilationmulti-model ensemblesThese were tested on the case studies in the North Sea and Caribbean Sea. Chaotic models appear to be are accurate and reliable short and mid-term predictors of storm surges aimed at supporting decision-makers for flood prediction and ship navigation.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 200
• Erscheinungstermin: 16. Dezember 2011
• Englisch
• ISBN-13: 9781466553484
• Artikelnr.: 38318030

Autorenporträt

Michael Siek earned his B.Sc.degree in Mathematics from Airlangga University and B.Com. degree in Information Management from STIKOM Institute, both in 2000 and M.Sc. degree in Hydroinformatics from UNESCO-IHE, The Netherlands in 2003. He received his Ph.D. degree in Hydroinformatics from Delft University of Technology (TUDelft) and UNESCO-IHE in 2011 with the thesis entitled "Predicting storm surges: chaos, computational intelligence, data assimilation, ensembles". Previously, he worked as a full-time lecturer at University of Surabaya and a visiting lecturer at Petra Christian University in the Faculty of Engineering and Faculty of Economics.

His research has spanned a large number of disciplines, emphasizing data-driven and physically-based modelling, hydrological and coastal modelling, nonlinear dynamics and chaos theory, computational intelligence, optimization techniques, data mining, data assimilation, multi-model ensemble predictions with a wide range of real-life applications.