Predicting significant wave height with artificial neural networks in the South Atlantic Ocean: a hybrid approach

Accurate simulations of significant wave height (Hs) are extremely important for the safety of navigation, port operations, and oil and gas exploration. Thus, accurate forecasts of Hs are essential for accident prevention and maintenance of services vital to the economy. Considering the limitations...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Ocean dynamics 2023-06, Vol.73 (6), p.303-315
Hauptverfasser: Marangoni Gazineu Marinho Pinto, Paula, Martins Campos, Ricardo, Gallo, Marcos Nicolas, Parente Ribeiro, Carlos Eduardo
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Accurate simulations of significant wave height (Hs) are extremely important for the safety of navigation, port operations, and oil and gas exploration. Thus, accurate forecasts of Hs are essential for accident prevention and maintenance of services vital to the economy. Considering the limitations of traditional numerical modeling, such as the typical model underestimation of Hs under severe conditions, forecasting Hs using artificial neural networks is a promising method and a complementary approach. In this study we develop a post-processing model using Long Short-Term Memory (LSTM) algorithm to improve outputs from the numerical model WAVEWATCH III (WW3) at Santos Basin, Brazil. The hybrid scheme is focused on the simulations of 1-, 2-, 3- and 4-day residues (difference between observations and WW3) using measurements from a local wave buoy moored in deep water. The results of the hybrid model (WW3+LSTM) show a better performance compared with WW3, being capable of better representing the peak of the events and storms. On average, the gains from using WW3+LSTM reach 3.8% in Correlation Coefficient (CORR), 14.2% in Bias (BIAS), 10.2% in Root Mean Squared Error (RMSE), and 10.7% in Scatter Index (SI). The hybrid model developed allows high-skill forecasts to be carried out on large domains and through longer horizons.
ISSN:1616-7341
1616-7228
DOI:10.1007/s10236-023-01546-y