Prediction of Sea Level Using Double Data Decomposition and Hybrid Deep Learning Model for Northern Territory, Australia

Nawin Raj (), Jaishukh Murali, Lila Singh-Peterson and Nathan Downs
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Nawin Raj: School of Mathematics, Physics and Computing, Springfield Campus, University of Southern Queensland, Springfield, QLD 4300, Australia
Jaishukh Murali: School of Mathematics, Physics and Computing, Springfield Campus, University of Southern Queensland, Springfield, QLD 4300, Australia
Lila Singh-Peterson: School of Agriculture and Environmental Science, Springfield Campus, University of Southern Queensland, Springfield, QLD 4300, Australia
Nathan Downs: School of Mathematics, Physics and Computing, Springfield Campus, University of Southern Queensland, Springfield, QLD 4300, Australia

Mathematics, 2024, vol. 12, issue 15, 1-25

Abstract: Sea level rise (SLR) attributed to the melting of ice caps and thermal expansion of seawater is of great global significance to vast populations of people residing along the world’s coastlines. The extent of SLR’s impact on physical coastal areas is determined by multiple factors such as geographical location, coastal structure, wetland vegetation and related oceanic changes. For coastal communities at risk of inundation and coastal erosion due to SLR, the modelling and projection of future sea levels can provide the information necessary to prepare and adapt to gradual sea level rise over several years. In the following study, a new model for predicting future sea levels is presented, which focusses on two tide gauge locations (Darwin and Milner Bay) in the Northern Territory (NT), Australia. Historical data from the Australian Bureau of Meteorology (BOM) from 1990 to 2022 are used for data training and prediction using artificial intelligence models and computation of mean sea level (MSL) linear projection. The study employs a new double data decomposition approach using Multivariate Variational Mode Decomposition (MVMD) and Successive Variational Mode Decomposition (SVMD) with dimensionality reduction techniques of Principal Component Analysis (PCA) for data modelling using four artificial intelligence models (Support Vector Regression (SVR), Adaptive Boosting Regressor (AdaBoost), Multilayer Perceptron (MLP), and Convolutional Neural Network–Bidirectional Gated Recurrent Unit (CNN-BiGRU). It proposes a deep learning hybrid CNN-BiGRU model for sea level prediction, which is benchmarked by SVR, AdaBoost, and MLP. MVMD-SVMD-CNN-BiGRU hybrid models achieved the highest performance values of 0.9979 (d), 0.996 (NS), 0.9409 (L); and 0.998 (d), 0.9959 (NS), 0.9413 (L) for Milner Bay and Darwin, respectively. It also attained the lowest error values of 0.1016 (RMSE), 0.0782 (MABE), 2.3699 (RRMSE), and 2.4123 (MAPE) for Darwin and 0.0248 (RMSE), 0.0189 (MABE), 1.9901 (RRMSE), and 1.7486 (MAPE) for Milner Bay. The mean sea level (MSL) trend analysis showed a rise of 6.1 ± 1.1 mm and 5.6 ± 1.5 mm for Darwin and Milner Bay, respectively, from 1990 to 2022.

Keywords: deep learning (DL); sea level (SL); convolutional neural network (CNN); bidirectional gated recurrent unit; support vector regression (SVR); adaptive boosting regressor (AdaBoost); multivariate variational mode decomposition (MVMD); successive variational mode decomposition (SVMD); principal component analysis (PCA) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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