%P 6892-6914
%I Taylor and Francis Ltd.
%D 2022
%R 10.1080/10106049.2021.1958015
%A N. Adebisi
%A A.-L. Balogun
%O cited By 5
%N 23
%K air-sea interaction; future prospect; holistic approach; mapping method; sea level change; trend analysis, Malaysia
%T A deep-learning model for national scale modelling and mapping of sea level rise in Malaysia: the past, present, and future
%V 37
%J Geocarto International
%X 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. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
%L scholars17895