Evaluation of multivariate adaptive regression splines and artificial neural network for prediction of mean sea level trend around northern Australian coastlines
Article
Raj, Nawin and Gharineiat, zahra. 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), pp. 1-20. https://doi.org/10.3390/math9212696
| Article Title | Evaluation of multivariate adaptive regression splines and artificial neural network for prediction of mean sea level trend around northern Australian coastlines |
|---|---|
| ERA Journal ID | 213646 |
| Article Category | Article |
| Authors | Raj, Nawin (Author) and Gharineiat, zahra (Author) |
| Journal Title | Mathematics |
| Journal Citation | 9 (21), pp. 1-20 |
| Article Number | 2696 |
| Number of Pages | 20 |
| Year | 2021 |
| Publisher | MDPI AG |
| Place of Publication | Basel, Switzerland |
| ISSN | 2227-7390 |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/math9212696 |
| Web Address (URL) | https://www.mdpi.com/2227-7390/9/21/2696 |
| 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. |
| Keywords | ANN; MARS; mean sea level; prediction; Australia; tide gauge |
| ANZSRC Field of Research 2020 | 370803. Physical oceanography |
| 370603. Geodesy | |
| 461104. Neural networks | |
| Public Notes | Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article |
| Byline Affiliations | School of Sciences |
| School of Civil Engineering and Surveying | |
| Institution of Origin | University of Southern Queensland |
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