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.

摘要

在这项研究中，我们对马来西亚海岸线沿线的 21 个站点的沿海海平面当前和未来趋势进行了整体评估。对于海平面预测，使用绝对海平面数据和海洋大气变量训练单变量和多变量长短期记忆 (LSTM) 神经网络的 3 个场景。四种情景预测模型的结果表明，使用组合海洋-大气变量训练的多元 LSTM 神经网络在模拟海平面变化方面表现最佳，平均 RMSE 和 R 精度分别为 0.060 和 0.861。根据各站海平面趋势平均值估算的全国海平面上升，相对海平面上升3.72毫米/年，绝对海平面上升3.68毫米/年。