Journal of Hydroinformatics 2 (2000) 61-84

Forecasting high waters at Venice Lagoon using chaotic time series analysis and nonlinear neural networks

J. M. Zaldívar, E. Gutiérrez, I. M. Galván, F.Strozzi and A. Tomasin

European Commission, Joint Research Centre, Institute for Systems, Informatics and Safety, TP 250, 21020–Ispra (VA), Italy

European Commission, Joint Research Centre, Institute for Systems, Informatics and Safety, TP 250, 21020–Ispra (VA), Italy

Department of Computer Science, Carlos III University of Madrid, C/ Butarque 15, 28911–Leganés, Madrid, Spain

Libero Istituto Universitario Carlo Cattaneo, Engineering Department, Quantitative Methods Group, 21053–Castellanza (VA), Italy

CNR-ISDGM Università Ca'Foscari Dorso Duro 2137, 30123–Venice, Italy

Time series analysis using nonlinear dynamics systems theory and multilayer 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 time delay embedding vectors and on the characterisation of invariant properties which define its dynamics. The results suggest the existence of a low dimensional chaotic attractor with a Lyapunov dimension, D_L, 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 autoregressive models (NAR) based on multilayer feedforward neural networks have been developed. From the study, it can be observed that nonlinear forecasting produces adequate results for the 'normal' dynamic behaviour of the water level of Venice Lagoon, outperforming linear algorithms, however, both methods fail to forecast the 'high water' phenomenon more than 2-3 hours ahead.

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