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.

威尼斯市是非凡的建筑，艺术和文化遗产。不幸的是，其保护受到越来越严重的高潮的威胁。预测威尼斯，尤其是高潮区域的潮汐水平，对保护城市和泻湖至关重要。为此，目前需要大量的输入数据的复杂统计或流体动力学模型。基于人工智能算法的模型可以提供有效的替代方案。在这项研究中，开发了几种不同的预测模型，并且每种模型都构建为三个变体，从而改变了已实施的机器学习算法：M5P回归树，随机森林和多层感知器。到目前为止，还没有使用回归树模型来预测潮位。事实证明，所有提出的模型都可以准确预测威尼斯的潮汐水平。在大多数情况下，M5P算法可提供最佳性能。所有基于M5P的模型的特征在于确定系数在0.924至0.996之间，而相对绝对误差在5.98至26.84％之间。此外，即使在水位极高的情况下，通过忽略气象因素也能获得良好的预测。最后，在数小时的预测范围内也获得了令人满意的结果，而进一步的具体比较表明，在考虑高水位预测时，基于考虑的机器学习算法的模型在外源输入变量的情况下能够胜过自回归综合移动平均模型。