Assessment and Prediction of Sea Level Trend in the South Pacific Region

Raj, Nawin ORCID: https://orcid.org/0000-0002-8364-2644 and Gharineiat, Zahra ORCID: https://orcid.org/0000-0003-0913-151X and Ahmed, Abul Abrar Masrur and Stepanyants, Yury ORCID: https://orcid.org/0000-0003-4546-0310 (2022) Assessment and Prediction of Sea Level Trend in the South Pacific Region. Remote Sensing, 14 (4):986. pp. 1-25.

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Abstract

Sea level rise is an important and topical issue in the South Pacific region and needs an urgent assessment of trends for informed decision making. This paper presents mean sea level trend assessment using harmonic analysis and a hybrid deep learning (DL) model based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique, Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Neighbourhood Component Analysis (NCA) to build a highly accurate sea level forecasting model for three small islands (Fiji, Marshall Island and Papua New Guinea (PNG)) in the South Pacific. For a 20-year period, the estimated mean sea level rise per year from the harmonic computation is obtained: 112 mm for PNG, 98 mm for Marshall Island and 52 mm for Fiji. The DL procedure uses climate and environment-based remote sensing satellite (MODIS, GLDAS-2.0, MODIS TERRA, MERRA-2) predictor variables with tide gauge base mean-sea level (MSL) data for model training and development for forecasting. The developed CEEMDAN-CNN-GRU as the objective model is benchmarked by comparison to the hybrid model without data decomposition, CNN-GRU and standalone models, Decision Trees (DT) and Support Vector Regression (SVR). All model performances are evaluated using reliable statistical metrics. The CEEMDAN-CNN-GRU shows superior accuracy when compared with other standalone and hybrid models. It shows an accuracy of >96% for the correlation coefficient and an error of < 1% for all study sites.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Date Deposited: 15 Mar 2022 23:41
Last Modified: 15 Mar 2022 23:59
Uncontrolled Keywords: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN); Convolutional Neural Network (CNN); Deep learning (DL); Gated Recurrent Unit (GRU); Mean sea level (MSL); Neigh-bourhood Component Analysis (NCA)
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460501 Data engineering and data science
37 EARTH SCIENCES > 3708 Oceanography > 370803 Physical oceanography
Socio-Economic Objectives (2020): 19 ENVIRONMENTAL POLICY, CLIMATE CHANGE AND NATURAL HAZARDS > 1904 Natural hazards > 190404 Hydrological hazards (e.g. avalanches and floods)
28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280107 Expanding knowledge in the earth sciences
Identification Number or DOI: https://doi.org/10.3390/rs14040986
URI: http://eprints.usq.edu.au/id/eprint/47128

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