Evaluation of multivariate adaptive regression splines and artificial neural network for prediction of mean sea level trend around northern Australian coastlines

Raj, Nawin ORCID: https://orcid.org/0000-0002-8364-2644 and Gharineiat, zahra ORCID: https://orcid.org/0000-0003-0913-151X (2021) Evaluation of multivariate adaptive regression splines and artificial neural network for prediction of mean sea level trend around northern Australian coastlines. Mathematics, 9 (21):2696. pp. 1-20.

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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 (R2 > 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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 - 31 Dec 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 - 31 Dec 2021)
Date Deposited: 26 Oct 2021 06:41
Last Modified: 23 Feb 2022 00:45
Uncontrolled Keywords: ANN; MARS; mean sea level; prediction; Australia; tide gauge
Fields of Research (2008): 04 Earth Sciences > 0405 Oceanography > 040503 Physical Oceanography
01 Mathematical Sciences > 0102 Applied Mathematics > 010299 Applied Mathematics not elsewhere classified
Fields of Research (2020): 37 EARTH SCIENCES > 3708 Oceanography > 370803 Physical oceanography
37 EARTH SCIENCES > 3706 Geophysics > 370603 Geodesy
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970101 Expanding Knowledge in the Mathematical Sciences
D Environment > 96 Environment > 9603 Climate and Climate Change > 960399 Climate and Climate Change not elsewhere classified
Socio-Economic Objectives (2020): 19 ENVIRONMENTAL POLICY, CLIMATE CHANGE AND NATURAL HAZARDS > 1999 Other environmental policy, climate change and natural hazards > 199999 Other environmental policy, climate change and natural hazards not elsewhere classified
28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280107 Expanding knowledge in the earth sciences
Identification Number or DOI: https://doi.org/10.3390/math9212696
URI: http://eprints.usq.edu.au/id/eprint/44001

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