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A deep-learning model for national scale modelling and mapping of sea level rise in Malaysia: the past, present, and future.
- Source :
-
Geocarto International . Dec2022, Vol. 37 Issue 23, p6892-6914. 23p. - Publication Year :
- 2022
-
Abstract
- In this study, we conducted a holistic evaluation of current and future trend in coastal sea level at the 21 stations along Malaysia's coastline. For sea level prediction, univariate and 3 scenarios of multivariate Long Short Term Memory (LSTM) neural networks were trained with absolute sea level data and ocean-atmospheric variables. The result from the four scenario predictive models revealed that multivariate LSTM neural network trained with combined ocean-atmospheric variables performed best for modelling sea level variation, giving a mean RMSE and R accuracy of 0.060 and 0.861, respectively. The national sea level rise estimated from the average of sea level trend at all stations is 3.72 mm/yr for relative sea level and 3.68 mm/yr for absolute sea level. The 2050 and 2100 projections indicate that sea level will continue to rise but at a very slow rate with no acceleration. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10106049
- Volume :
- 37
- Issue :
- 23
- Database :
- Academic Search Index
- Journal :
- Geocarto International
- Publication Type :
- Academic Journal
- Accession number :
- 159176711
- Full Text :
- https://doi.org/10.1080/10106049.2021.1958015