SummaryCoastal floods are regarded as among the most dangerous and harmful of all natural disasters affecting urban areas adjacent to the shorelines. Rapid urbanization combined with climate change and poor governance often results in significant increase of flood risk, especially for coastal communities. Wave overtopping and wave run-up are the key mechanisms for monitoring the results of coastal flooding and as such, significant efforts are currently focusing on their predicting. In this paper, an integrated methodology is proposed, accounting for wave overtopping and wave run-up under extreme wave scenarios caused by storm surges. By taking advantage of past and future climatic projections of wind data, a downscaling approach is proposed, utilizing a number of appropriate numerical models than can simulate the wave propagation from offshore up to the swash zone. The coastal zone of Rethymno in Greece is selected as a case study area and simulations of wave characteristics with the model SWAN for the period 1960–2100 in the offshore region are presented. These data are given as boundary conditions to further numerical models (MIKE21 PMS and HD) in order to investigate the spatial evolution of the wave and the hydrodynamic field in intermediate and shallow waters. Finally, the calculated wave height serves as input to empirical formulas and time dependent wave propagation models (MIKE21 BW) to estimate the wave run-up and wave overtopping (EurOtop). It is suggested that the proposed procedure is generic enough to be applicable to any similar region

Integrating renewable energy sources with aquaculture systems on floating multi-use platforms presents an innovative approach to developing sustainable and resilient offshore infrastructure, utilizing the ocean’s considerable potential. From March 2021 to January 2022, a 1:15-scale prototype was tested in Reggio Calabria, Italy, which gave crucial insights into how these structures behave under different wave conditions. This study investigates the application of Artificial Neural Networks (ANNs) to predict changes in mooring loads, particularly at key points of the structure. By analyzing metocean data, several ANN models and optimization techniques were evaluated to identify the most accurate predictive model. With a Normalized Root Mean Square Error (NRMSE) of 1.7–4.7%, the results show how ANNs can effectively predict offshore platform dynamics. This research highlights the potential of machine learning in developing and managing sustainable ocean systems, setting the stage for future advancements in data-driven marine resource management.