An EEMD-BiLSTM Algorithm Integrated with Boruta Random Forest Optimiser for Significant Wave Height Forecasting along Coastal Areas of Queensland, Australia

An EEMD-BiLSTM Algorithm Integrated with Boruta Random Forest Optimiser for Significant Wave Height Forecasting along Coastal Areas of Queensland, Australia

Using advanced deep learning (DL) algorithms for forecasting significant wave height of coastal sea waves over a relatively short period can generate important information on its impact and behaviour. This is vital for prior planning and decision making for events such as search and rescue and wave...

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Bibliographic Details
Main Authors: Nawin Raj, Jason Brown
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Remote Sensing
Subjects:
significant wave height (Hs)
boruta random forest optimiser (BRF)
ensemble empirical model decomposition (EEMD)
deep learning (DL)
bidirectional long short-term-memory (BiLSTM)
support vector regression (SVR)
Online Access:https://www.mdpi.com/2072-4292/13/8/1456

Internet

https://www.mdpi.com/2072-4292/13/8/1456

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