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

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, Dar...

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Autores principales:	Nawin Raj, Zahra Gharineiat
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	ANN MARS mean sea level prediction Australia tide gauge Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/16eaebc6ed6a4d608f6ea2b2491b7ab0
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id	oai:doaj.org-article:16eaebc6ed6a4d608f6ea2b2491b7ab0
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spelling	oai:doaj.org-article:16eaebc6ed6a4d608f6ea2b2491b7ab02021-11-11T18:15:37ZEvaluation of Multivariate Adaptive Regression Splines and Artificial Neural Network for Prediction of Mean Sea Level Trend around Northern Australian Coastlines10.3390/math92126962227-7390https://doaj.org/article/16eaebc6ed6a4d608f6ea2b2491b7ab02021-10-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2696https://doaj.org/toc/2227-7390Mean 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<sup>2</sup> > 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.Nawin RajZahra GharineiatMDPI AGarticleANNMARSmean sea levelpredictionAustraliatide gaugeMathematicsQA1-939ENMathematics, Vol 9, Iss 2696, p 2696 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ANN MARS mean sea level prediction Australia tide gauge Mathematics QA1-939
spellingShingle	ANN MARS mean sea level prediction Australia tide gauge Mathematics QA1-939 Nawin Raj Zahra Gharineiat Evaluation of Multivariate Adaptive Regression Splines and Artificial Neural Network for Prediction of Mean Sea Level Trend around Northern Australian Coastlines
description	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<sup>2</sup> > 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.
format	article
author	Nawin Raj Zahra Gharineiat
author_facet	Nawin Raj Zahra Gharineiat
author_sort	Nawin Raj
title	Evaluation of Multivariate Adaptive Regression Splines and Artificial Neural Network for Prediction of Mean Sea Level Trend around Northern Australian Coastlines
title_short	Evaluation of Multivariate Adaptive Regression Splines and Artificial Neural Network for Prediction of Mean Sea Level Trend around Northern Australian Coastlines
title_full	Evaluation of Multivariate Adaptive Regression Splines and Artificial Neural Network for Prediction of Mean Sea Level Trend around Northern Australian Coastlines
title_fullStr	Evaluation of Multivariate Adaptive Regression Splines and Artificial Neural Network for Prediction of Mean Sea Level Trend around Northern Australian Coastlines
title_full_unstemmed	Evaluation of Multivariate Adaptive Regression Splines and Artificial Neural Network for Prediction of Mean Sea Level Trend around Northern Australian Coastlines
title_sort	evaluation of multivariate adaptive regression splines and artificial neural network for prediction of mean sea level trend around northern australian coastlines
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/16eaebc6ed6a4d608f6ea2b2491b7ab0
work_keys_str_mv	AT nawinraj evaluationofmultivariateadaptiveregressionsplinesandartificialneuralnetworkforpredictionofmeansealeveltrendaroundnorthernaustraliancoastlines AT zahragharineiat evaluationofmultivariateadaptiveregressionsplinesandartificialneuralnetworkforpredictionofmeansealeveltrendaroundnorthernaustraliancoastlines
_version_	1718431867801174016

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

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