<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "A deep-learning model for national scale modelling and mapping of sea level rise in Malaysia: the past, present, and future"^^ . "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."^^ . "2022" . . "37" . "23" . . "Taylor and Francis Ltd."^^ . . . "Geocarto International"^^ . . . "10106049" . . . . . . . . . . "A.-L."^^ . "Balogun"^^ . "A.-L. Balogun"^^ . . "N."^^ . "Adebisi"^^ . "N. Adebisi"^^ . . . . . "HTML Summary of #17895 \n\nA deep-learning model for national scale modelling and mapping of sea level rise in Malaysia: the past, present, and future\n\n" . "text/html" . .