2026-04-17T06:57:43Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4257452026-02-04T06:57:08Zcom_2072_1033col_2072_452950

A significant wave height data-driven modeling for digital twins of marine environment Neyestani, Arman Toma, Daniel Falahzadehabarghouee, Ahmad Daponte, Pasquale Río Fernández, Joaquín del De Vito, Luca Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica Universitat Politècnica de Catalunya. SARTI-MAR - Sistemes d'Adquisició Remota de dades i Tractament de la Informació en el Medi Marí Àrees temàtiques de la UPC::Informàtica Accuracy Observatories Neural networks Decision making Predictive models Logic gates Real-time systems Digital twins Safety Environmental monitoring This study explores the development and imple-mentation of a predictive modeling framework specifically for forecasting Significant Wave Height (VHMO) in marine environments, leveraging the capabilities of digital twins (DTs). The primary variable, VHMO, represents the average height of the highest one-third of waves observed and is a crucial indicator for maritime operations and safety. Utilizing a Gated Recurrent Unit (GRU) neural network, the model was trained and evaluated using three distinct Wave Height datasets from in situ sensors on Tarragona, Barcelona, and the EMSO-OBSEA observatory, at the western Mediterranean. The methodology involves rigorous data preprocessing, including normalization and sequence cre-ation, followed by the training and validation of the model to forecast Significant Wave Height. Outliers are detected through residual analysis, establishing a threshold based on the statistical distribution of residuals. The model demonstrates high accuracy and robustness to data gaps, with Pearson correlation coefficients of 0.93, 0.95, and 0.88 for the Tarragona, Barcelona, and OBSEA datasets, respectively. The findings highlight the model's efficacy in predictive accuracy, contributing to enhanced monitoring and decision-making in maritime operations. Postprint (published version) 2024 Conference report Neyestani, A. [et al.]. A significant wave height data-driven modeling for digital twins of marine environment. A: IEEE International Workshop on Metrology for the Sea. "2024 IEEE International Workshop on Metrology for the Sea: Portorož, October 14-16, 2024: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2024, p. 495-500. ISBN 979-8-3503-7900-6. DOI 10.1109/MetroSea62823.2024.10765714 . 979-8-3503-7900-6 https://hdl.handle.net/2117/425745 10.1109/MetroSea62823.2024.10765714 eng https://ieeexplore.ieee.org/document/10765714 Restricted access - publisher's policy 6 p. application/pdf Institute of Electrical and Electronics Engineers (IEEE)