Print Email Facebook Twitter Predicting Storm Surges: Chaos, Computational Intelligence, Data Assimilation, Ensembles Title Predicting Storm Surges: Chaos, Computational Intelligence, Data Assimilation, Ensembles Author Siek, M.B.L.A. Contributor Solomatine, D.P. (promotor) Faculty Civil Engineering and Geosciences Department Water Management Date 2011-12-06 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. Subject ocean wave predictionnonlinear dynamics and chaos theoryneural networksoptimizationdimensionality reductionphase error correctionincomplete time seriesmulti-model ensemble predictiondata-driven modellingcomputational intelligencehydroinformatics To reference this document use: http://resolver.tudelft.nl/uuid:dfaae28f-c2dd-4bdc-82d6-a1c1aa98fa26 Publisher CRC Press/Balkema ISBN 978-0-415-62102-1 Part of collection Institutional Repository Document type doctoral thesis Rights (c) 2011 Siek, M.B.L.A. Files PDF UNESCO-IHE_PhD_SIEK_THESIS.pdf 5.69 MB Close viewer /islandora/object/uuid:dfaae28f-c2dd-4bdc-82d6-a1c1aa98fa26/datastream/OBJ/view