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Evaluation of Multivariate Adaptive Regression Splines and Artificial Neural Network for Prediction of Mean Sea Level Trend around Northern Australian Coastlines

Author

Listed:
  • Nawin Raj

    (School of Sciences, Springfield Campus, University of Southern Queensland, Toowoomba, QLD 4300, Australia)

  • Zahra Gharineiat

    (School of Civil Engineering and Surveying, Springfield Campus, University of Southern Queensland, Toowoomba, QLD 4300, Australia)

Abstract

Mean sea level rise is a significant emerging risk from climate change. This research paper is based on the use of artificial intelligence models to assess and predict the trend on mean sea level around northern Australian coastlines. The study uses sea-level times series from four sites (Broom, Darwin, Cape Ferguson, Rosslyn Bay) to make the prediction. Multivariate adaptive regression splines (MARS) and artificial neural network (ANN) algorithms have been implemented to build the prediction model. Both models show high accuracy (R 2 > 0.98) and low error values (RMSE < 27%) overall. The ANN model showed slightly better performance compared to MARS over the selected sites. The ANN performance was further assessed for modelling storm surges associated with cyclones. The model reproduced the surge profile with the maximum correlation coefficients ~0.99 and minimum RMS errors ~4 cm at selected validating sites. In addition, the ANN model predicted the maximum surge at Rosslyn Bay for cyclone Marcia to within 2 cm of the measured peak and the maximum surge at Broome for cyclone Narelle to within 7 cm of the measured peak. The results are comparable with a MARS model previously used in this region; however, the ANN shows better agreement with the measured peak and arrival time, although it suffers from slightly higher predictions than the observed sea level by tide gauge station.

Suggested Citation

  • Nawin Raj & Zahra Gharineiat, 2021. "Evaluation of Multivariate Adaptive Regression Splines and Artificial Neural Network for Prediction of Mean Sea Level Trend around Northern Australian Coastlines," Mathematics, MDPI, vol. 9(21), pages 1-18, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2696-:d:663497
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    3. Keshtegar, Behrooz & Mert, Cihan & Kisi, Ozgur, 2018. "Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSM, MARS and M5 model tree," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 330-341.
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    Cited by:

    1. José A. Sáez & José L. Romero-Béjar, 2022. "Impact of Regressand Stratification in Dataset Shift Caused by Cross-Validation," Mathematics, MDPI, vol. 10(14), pages 1-14, July.

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