Abstract Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2 cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast.

中文翻译：

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结合最小二乘法-神经网络方法利用卫星测高法预测黄海海平面

摘要 准确预测沿海地区海平面上升，迫切需要可访问的高质量观​​测数据集和适当的建模过程。本研究的重点是开发和验证适用于短期预测黄海海平面变化的最小二乘神经网络组合方法，其中使用最小二乘法拟合和外推海平面变化的周期项和线性趋势模型，而残差项的预测由几种不同类型的人工神经网络执行。使用的输入和输出数据是从 ERS-1/2、Topex/Poseidon、Jason-1/2 和 Envisat 卫星测高任务得出的 1993 年至 2016 年黄海的海平面异常 (SLA) 时间序列。执行不同神经网络架构和学习算法的测试，以评估它们对预测 SLA 时间序列残差的适用性。不同的神经网络令人满意地提供了可靠的结果，所提出的组合方法的预测的均方根误差小于 2 cm，观察到的和预测的 SLA 之间的相关系数高达 0.87。结果证明了组合最小二乘神经网络方法对靠近海岸的海平面变化的短期预测的可靠性。不同的神经网络令人满意地提供了可靠的结果，所提出的组合方法的预测的均方根误差小于 2 cm，观察到的和预测的 SLA 之间的相关系数高达 0.87。结果证明了组合最小二乘神经网络方法对靠近海岸的海平面变化的短期预测的可靠性。不同的神经网络令人满意地提供了可靠的结果，所提出的组合方法的预测的均方根误差小于 2 cm，观察到的和预测的 SLA 之间的相关系数高达 0.87。结果证明了组合最小二乘神经网络方法对靠近海岸的海平面变化的短期预测的可靠性。