Predicting Storm Surges

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.

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.