Predicting Storm Surges: Chaos, Computational Intelligence, Data Assimilation and Ensembles - UNESCO-IHE PhD Thesis (Hardcover)

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.

General

Imprint:	Crc Press
Country of origin:	United Kingdom
Release date:	September 2017
First published:	2011
Authors:	Michael Siek
Dimensions:	246 x 174mm (L x W)
Format:	Hardcover
ISBN-13:	978-1-138-47523-6
Categories:	Books > Science & Mathematics > Mathematics > Applied mathematics > Chaos theory Books > Earth & environment > Earth sciences > The hydrosphere > Oceanography (seas) Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	1-138-47523-8
Barcode:	9781138475236

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