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.

Automated summary

In this study, a holistic evaluation of current and future trends in coastal sea level in Malaysia was conducted using multivariate neural network models. The results indicate that sea level will continue to rise at a slow rate with no acceleration.