Abstract

This study aims to integrate a broad spectrum of ocean-atmospheric variables to predict sea level variation along West Peninsular Malaysia coastline using machine learning and deep learning techniques. 4 scenarios of different combinations of variables such as sea surface temperature, sea surface salinity, sea surface density, surface atmospheric pressure, wind speed, total cloud cover, precipitation and sea level data were used to train ARIMA, SVR and LSTM neural network models. Results show that atmospheric processes have more influence on prediction accuracy than ocean processes. Combining ocean and atmospheric variables improves the model prediction at all stations by 1- 9% for both SVR and LSTM. The means of R accuracy of optimal performing LSTM, SVR and ARIMA models at all stations are 0.853, 0.748 and 0.710, respectively. Comparison of model performance shows that the LSTM model trained with ocean and atmospheric variables is optimal for predicting sea level variation at all stations except Pulua Langkawi where ARIMA model trained without ocean-atmospheric variables performed best due to the dominating tide influence. This suggests that performance and suitability of prediction models vary across regions and selecting an optimal prediction model depends on the dominant physical processes governing sea level variability in the area of investigation.

摘要

这项研究旨在利用机器学习和深度学习技术整合广泛的海洋-大气变量，以预测马来西亚西部半岛海岸线上的海平面变化。使用4种不同变量组合的方案来训练ARIMA，SVR和LSTM神经网络模型，这些变量组合包括海面温度，海面盐度，海面密度，海面大气压力，风速，总云量，降水量和海平面数据。结果表明，与海洋过程相比，大气过程对预测精度的影响更大。对于SVR和LSTM，将海洋和大气变量结合起来可以使所有站的模型预测提高1-9％。在所有站点上，最佳性能的LSTM，SVR和ARIMA模型的R精度均值分别为0.853、0.748和0.710。模型性能的比较表明，用海洋和大气变量训练的LSTM模型最适合预测所有站的海平面变化，但由于主要的潮汐影响，在不加海洋和大气变量的情况下训练的ARIMA模型表现最好的是Pulua Langkawi。这表明，预测模型的性能和适用性在不同地区之间存在差异，选择最佳预测模型取决于控制调查区域海平面变化的主要物理过程。