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Article

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

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School of Mathematics, Physics and Computing, Springfield Campus, University of Southern Queensland, Toowoomba, QLD 4300, Australia
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School of Civil Engineering and Surveying, Springfield Campus, University of Southern Queensland, Toowoomba, QLD 4300, Australia
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Department of Applied Mathematics, Nizhny Novgorod State Technical University n.a. R.E. Alekseev, 603155 Nizhny Novgorod, Russia
*
Author to whom correspondence should be addressed.
Academic Editor: Chung-yen Kuo
Remote Sens. 2022, 14(4), 986; https://doi.org/10.3390/rs14040986
Received: 11 January 2022 / Revised: 7 February 2022 / Accepted: 14 February 2022 / Published: 17 February 2022
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 correlation coefficient and an error of <1% for all study sites. View Full-Text
Keywords: mean sea level (MSL); Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN); Convolutional Neural Network (CNN); Gated Recurrent Unit (GRU); Neighbourhood Component Analysis (NCA); deep learning (DL) mean sea level (MSL); Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN); Convolutional Neural Network (CNN); Gated Recurrent Unit (GRU); Neighbourhood Component Analysis (NCA); deep learning (DL)
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MDPI and ACS Style

Raj, N.; Gharineiat, Z.; Ahmed, A.A.M.; Stepanyants, Y. Assessment and Prediction of Sea Level Trend in the South Pacific Region. Remote Sens. 2022, 14, 986. https://doi.org/10.3390/rs14040986

AMA Style

Raj N, Gharineiat Z, Ahmed AAM, Stepanyants Y. Assessment and Prediction of Sea Level Trend in the South Pacific Region. Remote Sensing. 2022; 14(4):986. https://doi.org/10.3390/rs14040986

Chicago/Turabian Style

Raj, Nawin, Zahra Gharineiat, Abul Abrar Masrur Ahmed, and Yury Stepanyants. 2022. "Assessment and Prediction of Sea Level Trend in the South Pacific Region" Remote Sensing 14, no. 4: 986. https://doi.org/10.3390/rs14040986

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