集成栈式自编码器与XGBoost的深度学习海浪有效波高预报模型

有效波高预报对人类海上活动和海洋工程都至关重要。人工神经网络在有效波高预报中得到广泛的应用，并取得了良好的效果。但是，它作为一种浅层的网络架构，表达能力有限，这使得预报准确性在不同区域中波动。因此，为了提高有效波高的总体预报准确性，本文提出一种集成栈式自编码器（SAE）和XGBoost的深度学习海浪有效波高预报模型。首先，利用SAE算法强大的特征表征能力处理海浪数据，实现数据的扩维表达。其次，将SAE深层的特征作为XGBoost算法的输入，进行有效波高预测。本文重点研究有效波高预报方法，并根据台湾海峡中部2号大浮标2017年全年的实测波浪资料进行研究。实验结果表明，本文方法在确定性系数（R^2）和均方误差（MSE）方面均优于现有方法。

Ensemble Stacked Autoencoders and XGBoost Based Deep Learning Model for Significant Wave Height Forecasting

The significant wave height forecast is crucial for both human marine activities and marine engineering. The artificial neural network has been widely used in significant wave height prediction and achieved good results. However, as a shallow network architecture, it has limited expressive ability, making the forecast accuracy fluctuate in different regions. Therefore, to improve the overall forecast accuracy of the significant wave height, this paper proposes a deep learning model of significant wave height forecasting by integrating stacked autoencoders (SAE) and XGBoost. First, the powerful feature representation capabilities of the SAE algorithm are used to process ocean wave data to realize the extended dimension expression of the data. Secondly, the deep feature expression of SAE is used as the input of the XGBoost algorithm to predict effective wave heights. This paper focuses on the significant wave height prediction method and uses the measured wave data of Buoy 2 in the central Taiwan Strait in 2017. The experimental results show that our approach is superior to existing methods in terms of deterministic coefficient (R^2) and mean square error (MSE).