Print Email Facebook Twitter Nonlinear chaotic model for predicting storm surges Title Nonlinear chaotic model for predicting storm surges Author Siek, M. Solomatine, D.P. Faculty Civil Engineering and Geosciences Department Water Management Abstract This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions. To reference this document use: http://resolver.tudelft.nl/uuid:73c6a461-b567-46bc-83d3-de2d02f27779 DOI https://doi.org/10.5194/npg-17-405-2010 Publisher European Geosciences Union (EGU) ISSN 1023-5809 Source http://www.nonlin-processes-geophys.net/17/405/2010/npg-17-405-2010.html Source Nonlinear Processes in Geophysics, 17 (5), 2010 Part of collection Institutional Repository Document type journal article Rights (c) 2010 The Author(s)Creative Commons Attribution 3.0 License Files PDF Solomatine_2010.pdf 1.23 MB Close viewer /islandora/object/uuid:73c6a461-b567-46bc-83d3-de2d02f27779/datastream/OBJ/view