2026-06-12T05:11:40Zhttps://iris.cnr.it/oai/request

oai:iris.cnr.it:20.500.14243/5709432026-03-04T14:31:49Zcom_20.500.14243_46com_20.500.14243_21col_20.500.14243_49ou_ou239ou_ou294ou_ou420ou_ou167

A preliminary analysis of high water events in Venice based on multi-decadal observations and clustering Cardillo Franco Alberto Andrigo Angela De Biasio Francesco Debole Franca Favaro Marco Papa Alvise Straccia Umberto Vignudelli Stefano European Geosciences Union (EGU) Cardillo, Franco Alberto Andrigo, Angela De Biasio, Francesco Debole, Franca Favaro, Marco Papa, Alvise Straccia, Umberto Vignudelli, Stefano Machine learning Clustering High water events in Venice are a recurrent phenomenon, as the city is located only slightly above mean sea level and is directly in"uenced by water-level variations within the lagoon. Repeated "ooding has signi!cant economic and social impacts, limits pedestrian and naval tra#c and contributes to the degradation of buildings and cultural heritage. Current forecasting systems primarily estimate water levels and peak values, and these are typically estimated at a limited number of locations. Data-driven approaches, in particular Machine Learning (ML) methods, analyze historical data without relying on prede!ned, human-designed model structures. We present a preliminary analysis based on several clustering approaches, including k-means, DBSCAN, and deep learning–based methods, applied to a multi-decadal atmospheric dataset and to the longest available reconstructed hourly sea-level records for the northern Adriatic Sea, specifically developed for this study. 2026 info:eu-repo/semantics/conferenceObject https://hdl.handle.net/20.500.14243/570943 eng ispartofbook:Proceedings of the EGU General Assembly 2026 EGU General Assembly 2026 issue:17357 numberofpages:1 info:eu-repo/semantics/openAccess ELETTRONICO license:Creative commons license uri:http://creativecommons.org/licenses/by/4.0/