The intensification of extreme events, storm surges and coastal flooding in a climate change scenario increasingly influences human processes, especially in coastal areas where sea-based activities are concentrated. Predicting sea level near the coasts, with a high accuracy and in a reasonable amount of time, becomes a strategic task. Despite the developments of complex numerical codes for high-resolution ocean modeling, the task of making forecasts in areas at the intersection between land and sea remains challenging. In this respect, the use of machine learning techniques can represent an interesting alternative to be investigated and evaluated by numerical modelers.

This article presents the application of the Long-Short Term Memory (LSTM) neural network to the problem of short-term sea level forecasting in the Southern Adriatic Northern Ionian (SANI) domain in the Mediterranean sea. The proposed multi-model architecture based on LSTM networks has been trained to predict mean sea levels three days ahead, for different coastal locations. Predictions were compared with the observation data collected through the tide-gauge devices as well as with the forecasts produced by the Southern Adriatic Northern Ionian Forecasting System (SANIFS) developed at the Euro-Mediterranean Center on Climate Change (CMCC), which provides short-term daily updated forecasts in the Mediterranean basin. Experimental results demonstrate that the multi-model architecture is able to bridge information far in time and to produce predictions with a much higher accuracy than SANIFS forecasts.

在气候变化情况下，极端事件，风暴潮和沿海洪水的加剧，对人类进程的影响越来越大，特别是在海上活动集中的沿海地区。高精度并在合理的时间内预测海岸附近的海平面已成为一项战略任务。尽管为高分辨率海洋建模开发了复杂的数字代码，但在陆地与海洋相交处的区域进行预测的任务仍然具有挑战性。在这方面，使用机器学习技术可以代表一个有趣的替代方案，供数值建模人员研究和评估。

本文介绍了长期记忆（LSTM）神经网络在地中海南部亚得里亚海北爱奥尼亚（SANI）域中短期海平面预测问题中的应用。已经对基于LSTM网络的拟议多模型架构进行了培训，以预测三天前不同沿海地区的平均海平面。将该预测结果与潮汐仪收集到的观测数据以及由欧洲地中海气候变化中心（CMCC）开发的南亚得里亚海北部爱奥尼亚州预报系统（SANIFS）产生的预报进行了比较，该预报提供了短时预报。地中海盆地的长期每日更新预报。