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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Nawin Raj & Jaishukh Murali & Lila Singh-Peterson & Nathan Downs, 2024. "Prediction of Sea Level Using Double Data Decomposition and Hybrid Deep Learning Model for Northern Territory, Australia," Mathematics, MDPI, vol. 12(15), pages 1-25, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:15:p:2376-:d:1446263
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    References listed on IDEAS

    as
    1. Kerstin Zander & Lisa Petheram & Stephen Garnett, 2013. "Stay or leave? Potential climate change adaptation strategies among Aboriginal people in coastal communities in northern Australia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 67(2), pages 591-609, June.
    2. Nawin Raj, 2022. "Prediction of Sea Level with Vertical Land Movement Correction Using Deep Learning," Mathematics, MDPI, vol. 10(23), pages 1-23, November.
    3. Qiongjie Kou & Quanyou Zhang & Laiqun Xu & Yaohui Li & Yong Feng & Huiting Wei, 2022. "Mobile Learning Strategy Based on Principal Component Analysis," International Journal of Information Systems in the Service Sector (IJISSS), IGI Global, vol. 14(3), pages 1-12, July.
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