Forecasting high waters at Venice Lagoon using chaotic time series analysis and nonlinear neural networks
Time series analysis using nonlinear dynamics systems theory and multi layer neural networks models have been applied to the time sequence of water level data recorded every hour at 'Punta della Salute' from Venice Lagoon during the years 1980-1994. The first method is based on the reconstruction of the state space attractor using the time delay embedding vectors and on the characterization of invariant properties which define its dynamics. The results suggest the existence of a low dimensional chaotic attractor with a Lyapunov dimension of around 6.6 and a predictability between 8 and 13 hours ahead. Furthermore, once the attractor has been reconstructed, it is possible to make predictions by mapping local-neighbourhood to local-neighbourhood in the reconstructed phase space. To compare the prediction results with another nonlinear method, two nonlinear autoaggressive models based on multilayer feed forward neural networks have been developed.
Bibliographic Reference: Article: Journal of Hydro-informatics (2000) 61-83
Record Number: 200011853 / Last updated on: 2000-03-21
Original language: en
Available languages: en