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 correlation coefficient and an error of <1% for all study sites.
Highlights
Introduction published maps and institutional affilClimatic change has a considerable influence on the coastal communities of Australia and Small Island Developing States (SIDS) in the South Pacific
TrendsThe shown in these figures with error uncertainty agree with increase for26 the past The trends shown in these figures with error uncertainty what was obtained in for the rate of
The developed CEEMDAN-Convolutional Neural Network (CNN)-Gated Recurrent Unit (GRU) prediction model implemented for the testing phase captures high values of correlation coefficient (r), Nash–Sutcliffe efficiency
Summary
Introduction published maps and institutional affilClimatic change has a considerable influence on the coastal communities of Australia and Small Island Developing States (SIDS) in the South Pacific. The changes in sea level extremes depend on many environmental and climatic variables, such as increasing global temperatures Among these, events such as tropical cyclones, storm surges and wave-breaking processes can cause a devastating impact on small islands [3,4]. According to [5], rising oceanic temperatures affect the marine ecosystem in many ways, one of which is the melting of ice caps driving sea levels to rise. This rise varies widely, the cumulative effect could lead to serious consequences in the South Pacific region
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