Optimizing Neural Network Training Data Selection for Nearshore Sea State Forecasting Through Clustering Algorithms
Year: 2025
Type: article
Abstract: The growth of maritime traffic led to increased maritime accidents and multiple factors, including weather conditions, can contribute to these accidents. To mitigate the accidents in port areas, the scientific community is dedicated to recognizing risks and formulating effective strategies for prevention (Marino et al., 2023). Therefore, understanding local weather and sea conditions well in advance can significantly reduce the likelihood of accidents in port areas. Nevertheless, the ability to forecast sea conditions in deep water, where wave-seabed interactions are irrelevant, is generally available. Using this information, numerical models assess the sea state in the nearby coastal areas. However, these models require extensive computational resources and are unsuitable for immediate short-term predictions and forecasting systems (Salah et al., 2016).
Source: Coastal Engineering Proceedings
Authors: Elisa Castro, Luca Cavallaro, Claudio Iuppa, David Lucio Fernández, Javier López Lara, Enrico Foti, Rosaria Ester Musumeci
Language: English
Subfield: Oceanography
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410869905