Ensemble-Empirical-Mode-Decomposition (EEMD) on SWH prediction: The effect of decomposed IMFs, continuous prediction duration, and data-driven models

• Published in: Ocean engineering Vol. 324; p. 120755
• Main Authors: Guo, Yuanye, Si, Jicang, Wang, Yulian, Hanif, Farhan, Li, Shuang, Wu, Mengwei, Xu, Minyi, Mi, Jianchun
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 30.04.2025
• Subjects: Continuous prediction; Ensemble Empirical Mode Decomposition(EEMD); IMF decomposition; Machine learning; Significant wave height(SWH) prediction; Significant wave height(SWH) prediction; Ensemble Empirical Mode Decomposition(EEMD); Continuous prediction; Machine learning; IMF decomposition

This paper systematically investigates the impact of key factors on predicting significant wave height (SWH) using Ensemble Empirical Mode Decomposition (EEMD), specifically examining the number of decomposed Intrinsic Mode Functions (IMFs), the duration of continuous predictions, and the selection...