An EEMD-BiLSTM algorithm integrated with Boruta random forest optimiser for significant wave height forecasting along coastal areas of Queensland, Australia

Raj, Nawin and Brown, Jason (2021) An EEMD-BiLSTM algorithm integrated with Boruta random forest optimiser for significant wave height forecasting along coastal areas of Queensland, Australia. Remote Sensing, 13 (8):1456. pp. 1-20.

[img]
Preview
Text (Published Version)
remotesensing-13-01456 (1).pdf
Available under License Creative Commons Attribution 4.0.

Download (6MB) | Preview

Abstract

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 surges along the coastal environment. Short-term 24 h forecasting could provide adequate time for relevant groups to take precautionary action. This study uses features of ocean waves such as zero up crossing wave period (Tz), peak energy wave period (Tp), sea surface temperature (SST) and significant lags for significant wave height (Hs) forecasting. The dataset was collected from 2014 to 2019 at 30 min intervals along the coastal regions of major cities in Queensland, Australia. The novelty of this study is the development and application of a highly accurate hybrid Boruta random forest (BRF)–ensemble empirical mode decomposition (EEMD)–bidirectional long short-term memory (BiLSTM) algorithm to predict significant wave height (Hs). The EEMD–BiLSTM model outperforms all other models with a higher Pearson’s correlation (R) value of 0.9961 (BiLSTM—0.991, EEMD-support vector regression (SVR)—0.9852, SVR—0.9801) and comparatively lower relative mean square error (RMSE) of 0.0214 (BiLSTM—0.0248, EEMD-SVR—0.043, SVR—0.0507) for Cairns and similarly a higher Pearson’s correlation (R) value of 0.9965 (BiLSTM—0.9903, EEMD–SVR—0.9953, SVR—0.9935) and comparatively lower RMSE of 0.0413 (BiLSTM—0.075, EEMD-SVR—0.0481, SVR—0.057) for Gold Coast


Statistics for USQ ePrint 41910
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/).
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 -)
Date Deposited: 29 Apr 2021 04:22
Last Modified: 29 Apr 2021 04:22
Uncontrolled Keywords: 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)
Fields of Research (2008): 04 Earth Sciences > 0405 Oceanography > 040599 Oceanography not elsewhere classified
05 Environmental Sciences > 0502 Environmental Science and Management > 050299 Environmental Science and Management not elsewhere classified
01 Mathematical Sciences > 0102 Applied Mathematics > 010299 Applied Mathematics not elsewhere classified
Fields of Research (2020): 41 ENVIRONMENTAL SCIENCES > 4101 Climate change impacts and adaptation > 410199 Climate change impacts and adaptation not elsewhere classified
37 EARTH SCIENCES > 3708 Oceanography > 370899 Oceanography not elsewhere classified
49 MATHEMATICAL SCIENCES > 4901 Applied mathematics > 490199 Applied mathematics not elsewhere classified
Socio-Economic Objectives (2008): D Environment > 96 Environment > 9603 Climate and Climate Change > 960399 Climate and Climate Change not elsewhere classified
Socio-Economic Objectives (2020): 18 ENVIRONMENTAL MANAGEMENT > 1802 Coastal and estuarine systems and management > 180299 Coastal and estuarine systems and management not elsewhere classified
19 ENVIRONMENTAL POLICY, CLIMATE CHANGE AND NATURAL HAZARDS > 1901 Adaptation to climate change > 190101 Climate change adaptation measures (excl. ecosystem)
18 ENVIRONMENTAL MANAGEMENT > 1805 Marine systems and management > 180506 Oceanic processes (excl. in the Antarctic and Southern Ocean)
Identification Number or DOI: https://doi.org/10.3390/rs13081456
URI: http://eprints.usq.edu.au/id/eprint/41910

Actions (login required)

View Item Archive Repository Staff Only