Artificial Intelligence models for prediction of the tide level in Venice

The city of Venice is an extraordinary architectural, artistic and cultural heritage. Unfortunately, its conservation is increasingly threatened by particularly significant high tides. Predicting the tide level in Venice, especially the high waters, is an essential task for the protection of the city and the lagoon. Complex statistical or hydrodynamic models, which require a large amount of input data, are currently used for this purpose. An effective alternative can be provided by models based on Artificial Intelligence algorithms. In this study, several different forecasting models were developed and each model was built in three variants, varying the implemented machine learning algorithm: M5P Regression Tree, Random Forest and Multilayer Perceptron. Until now, regression tree models had never been used to forecast tide levels. All the proposed models proved to be able to forecast the tide level in Venice with good accuracy. The M5P algorithm provided the best performance in most cases. All the models based on M5P were characterized by a coefficient of determination between 0.924 and 0.996, while the Relative Absolute Error was between 5.98 and 26.84%. In addition, good predictions were achieved by neglecting meteorological factors, even in the case of exceptionally high waters. Finally, satisfactory outcomes were also obtained with a forecast horizon of several hours, while a further specific comparison showed that the models based on the considered Machine Learning algorithms are able to outperform the AutoRegressive Integrated Moving Average models with exogenous input variables in forecasting high water.

Automated summary

The study developed several different tidal forecast models, with models based on Artificial Intelligence algorithms performing well. The M5P algorithm showed the best performance in most cases, accurately predicting tide levels in Venice. Good predictions were achieved even when meteorological factors were neglected.