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

计算机与现代化 ›› 2021, Vol. 0 ›› Issue (04): 32-36.

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

(1.河海大学海岸灾害及防护教育部重点实验室,江苏南京210098;2.河海大学计算机与信息学院,江苏南京211100;
3.福建省海洋预报台,福建福州350003;4.河海大学港口海岸与近海工程学院,江苏南京210098)

出版日期:2021-04-22 发布日期:2021-04-25
作者简介:陆小敏(1995—)，女，安徽合肥人，硕士研究生，研究方向：数据挖掘，E-mail: 1846072423@qq.com; 刘凡(1988—)，男，江苏宿迁人，教授，博士，研究方向：计算机视觉，机器学习，多媒体分析与理解，E-mail: fanliu@hhu.edu.cn; 通信作者：李雪丁(1982—)，男，江西新余人，副研究员，硕士，研究方向：海洋预报，海洋遥感，E-mail: lxd007@xmu.edu.cn。
基金资助:
河海大学海岸灾害及防护教育部重点实验室开放基金资助项目(20150009); 福建省科技计划项目(2018Y0001)

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

(1. Key Laboratory of Coastal Disaster and Protection of Ministry of Education, Hohai University, Nanjing 210098, China;
2. College of Computer and Information, Hohai University, Nanjing 211100, China;
3. Fujian Marine Forecasts, Fuzhou 350003, China;
4. College of Harbour, Coastal and Offshore Engineering, Hohai University, Nanjing 210098, China)

Online:2021-04-22 Published:2021-04-25

摘要/Abstract

摘要： 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。

关键词: 有效波高, 栈式自编码器, XGBoost, 深度学习

Abstract: 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).

Key words: significant wave height, stacked autoencoders, XGBoost, deep learning

陆小敏, 刘凡, 蔡丽华, 李雪丁, 徐啸. 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[J]. 计算机与现代化, 2021, 0(04): 32-36.

LU Xiao-min, LIU Fan, CAI Li-hua, LI Xue-ding, XU Xiao. Ensemble Stacked Autoencoders and XGBoost Based Deep Learning Model for Significant Wave Height Forecasting[J]. Computer and Modernization, 2021, 0(04): 32-36.

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