High Tide Prediction

Description:

      Unusually high tides, or sea surges, result from a combination of chaotic climatic elements in conjunction with the more normal, periodic, tidal systems associated with a particular area. The prediction of such events has always been subject of intense interest to mankind, not only from human point of view, but also from an economic one. The most famous example of flooding in the Venice Lagoon occurred in November 1966 when, driven by strong winds, the Venice Lagoon rose by nearly 2 m above the normal water level. The damage to the city's homes, churches and museums ran into hundred of millions of Euros.

      Tide's behaviour is difficult to be predicted, because depends of too much factors, like the astronomic and atmospheric agents. The problem has been approached by numerical models and statistical methods. Numerical models require the computation of the meteorological forcing functions on each point of the finite difference grid and, hence, they are computationally expensive. Linear stochastic models are suitable for online forecasting since they are simple and their computation burden is low.

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Instances and best known solutions for those instances:

      In order to define an instance of this function we need to provide some examples of the series to predict. Right click on table to get the file which contains the level water in the Venice Lagoon measured each hour along the years 1980-1989 and 1990-1995 (Provided by A. Tomasin, CNR-ISDMG Università Ca'Foscari, Venice).

1980-1989	1990-1995

Related Papers:

[LIH04] C. Luque, P. Isasi, J.C. Hernández, "Forecasting Time Series by means of Evolutionary Algorithms", Proceedings of the 8th International Conference on Parallel Problem Solving from Nature (PPSN VIII) Spinger Verlag LNCS (2004), pp.1058-1067.

[ZGG00] J.M. Zaldívar, E. Gutiérrez, I.M. Galván, F. Strozzi, A. Tomasin, "Forecasting high waters an Venice Lagoon using chaotic time series analysis and nonlinear neural networks", Journal of Hydroinformatics 02.1 (2000), pp. 61-84.

[GIAV01] I.M. Galván, P. Isasi, R. Aler, J.M. Valls, "A selective learning method to improve the generalization of multilayer feedforward neural networks", International Journal of Neural Systems, Vol 11, No 2 (2001), pp. 167-177.

Click here to get the bibliography in bibtex fotmat.