Predicting Storm Surges

Chaos, Computational Intelligence, Data Assimilation, Ensembles

Doctoral thesis (2011)

Authors

Contributors

D.P. Solomatine (promotor)
Department
Water Management (CEG) (TU Delft)
Copyright: © 2011 Siek, M.B.L.A.
Neural networks Optimization Dimensionality reduction Computational intelligence Ocean wave prediction Nonlinear dynamics and chaos theory Phase error correction Incomplete time series Multi-model ensemble prediction Data-driven modelling Hydroinformatics
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Published Date

06-12-2011

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ISBN:

978-0-415-62102-1

Faculty

Civil Engineering and Geosciences

Department

Water Management

Abstract

Accurate predictions of storm surge are of importance in many 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 reduction, incomplete time series, phase error correction, finding true neighbours, optimization of chaotic model, data assimilation and multi-model ensembles. These 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.