Accurate predictions of storm surge are of importance in many coastal areas in the world to avoid and mitigate its destructive impacts. For this purpose the physically-based (process) numerical models are typically utilized. However, in data-rich cases, one may use data-driven methods aiming at reconstructing the internal patterns of the modelled processes and relationships between the observed descriptive variables. This book focuses on data-driven modelling using methods of nonlinear dynamics and chaos theory. First, some fundamentals of physical oceanography, nonlinear dynamics and chaos, computational intelligence and European operational storm surge models are covered. After that a number of improvements in building chaotic models are presented: nonlinear time series analysis, multi-step prediction, phase space dimensionality reduction, techniques dealing with incomplete time series, phase error correction, finding true neighbours, optimization of chaotic model, data assimilation and multi-model ensemble prediction. The major case study is surge prediction in the North Sea, with some tests on a Caribbean Sea case. The modelling results showed that the enhanced predictive chaotic models can serve as an efficient tool for accurate and reliable short and mid-term predictions of storm surges in order to support decision-makers for flood prediction and ship navigation.
By:
Michael Siek
Imprint: CRC Press
Country of Publication: United Kingdom
Dimensions:
Height: 246mm,
Width: 174mm,
Weight: 566g
ISBN: 9780415621021
ISBN 10: 041562102X
Pages: 200
Publication Date: 16 December 2011
Audience:
College/higher education
,
Primary
Format: Paperback
Publisher's Status: Active
CHAPTER 1: INTRODUCTION CHAPTER 2: CASE STUDY CHAPTER 3: STORM SURGE MODELINGCHAPTER 4: COMPUTATIONAL INTELLIGENCE CHAPTER 5: NONLINEAR DYNAMICS AND CHAOS THEORY CHAPTER 6: BUILDING PREDICTIVE CHAOTIC MODEL CHAPTER 7: ENHANCEMENTS: RESOLVING ISSUES OF HIGH DIMENSIONALITY, PHASE ERRORS, INCOMPLETENESS AND FALSE NEIGHBORS CHAPTER 8: COMPUTATIONAL INTELLIGENCE IN IDENTIFYING OPTIMALPREDICTIVE CHAOTIC MODEL CHAPTER 9: REAL-TIME DATA ASSIMILATION USING NARX NEURAL NETWORK CHAPTER 10: ENSEMBLE MODEL PREDICTION CHAPTER 11: CONCLUSIONS AND RECOMMENDATIONS
"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."
