Assessment of Medicane Helios meteo-marine parameters using a machine learning approach

被引:1
|
作者
Scardino, Giovanni [1 ]
Kushabaha, Alok [1 ,2 ]
Sabato, Gaetano [1 ]
Tarascio, Sebastiano [3 ]
Scicchitano, Giovanni [1 ]
机构
[1] Univ Bari Aldo Moro, Dept Earth & Geoenvironm Sci, I-70125 Bari, Italy
[2] IUSS Sch Adv Studies, Pavia, Italy
[3] Univ Catania, Dept Biol Geol & Environm Sci, I-95129 Catania, Italy
关键词
Neural Network; storm surge; flooding; tide phases; wave height; SEA; HURRICANE; CYCLONES;
D O I
10.1109/MetroSea58055.2023.10317554
中图分类号
P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Medicanes represent a unique weather phenomenon in the Mediterranean Sea, combining features of both tropical and extra-tropical cyclones. The recent Medicane Helios induced a severe windstorm that caused storm surges and flooding impacting coastal regions of southeastern Sicily. To assess the hydrodynamic parameters, such as tide phase, storm surge and wave flow induced by Medicane "Helios", an innovative machine learning system called LEUCOTEA was used. This system takes into account a combined approach of Geomorphological surveys, Convolution Neural Network, and Optical Flow techniques with real-time video records. The system provides the values of tide phases, storm surge and wave flow that could be useful for policymakers and environmental managers as a valuable tool to assess the potential coastal risks and to develop appropriate mitigation and adaptation strategies.
引用
收藏
页码:508 / 512
页数:5
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