海洋学研究 ›› 2024, Vol. 42 ›› Issue (3): 108-118.DOI: 10.3969/j.issn.1001-909X.2024.03.009

• 研究论文 • 上一篇    下一篇

利用机器学习模型预测中国沿海海平面变化

陈建珩(), 许东峰*(), 姚志雄   

  1. 自然资源部第二海洋研究所,卫星海洋环境动力学国家重点实验室,浙江 杭州 310012
  • 收稿日期:2023-11-23 修回日期:2024-01-30 出版日期:2024-09-15 发布日期:2024-11-25
  • 通讯作者: *许东峰(1966—),男,研究员,主要从事大洋环流和海气相互作用方面的研究,E-mail: xudongfengsio@sio.org.cn。
  • 作者简介:陈建珩(1998—),男,江苏省盐城市人,主要从事物理海洋方面的研究,E-mail: ycjhlaji@gmail.com。
  • 基金资助:
    浙江省财政一般公共预算项目(330000210130313013006)

Prediction of sea level changes along the coast of China using machine learning models

CHEN Jianheng(), XU Dongfeng*(), YAO Zhixiong   

  1. State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography,MNR, Hangzhou 310012, China
  • Received:2023-11-23 Revised:2024-01-30 Online:2024-09-15 Published:2024-11-25

RichHTML

4

PDF

17

摘要：

该文利用线性回归函数,根据卫星测高及中国沿海6个验潮站数据估算出1993—2020年中国沿海绝对海平面上升速率为4.17±1.32 mm/a,相对海平面上升速率为4.47±0.90 mm/a。将1958—2020年的大气数据、海洋数据及气候模态指数作为预报因子,建立了长短期记忆神经网络模型(LSTM模型)、循环神经网络模型(RNN模型)、门控循环单元神经网络模型(GRU模型)和支持向量机回归模型(SVR模型)等多种神经网络模型对中国沿海6个验潮站周边的相对海平面变化趋势进行预测。模型评估结果表明,同时引入大气变量、海洋变量及气候模态指数变量的LSTM模型取得的预测值与观测值的平均相关系数和均方根误差分别为0.866和19.279 mm,在4种模型中表现最佳,可以作为一种新型的预测相对海平面变化的方法。

关键词: 海平面上升, 相对海平面, 绝对海平面, 垂直地壳运动, 卫星测高, 潮位, LSTM神经网络模型, 时间序列预测

Abstract:

Based on the data of satellite altimetry and six tide gauge stations along the coast of China, linear regression function was used to estimate the absolute sea level rise rate in the coastal areas of China from 1993 to 2020, which was 4.17±1.32 mm/a, and the relative sea level rise rate was 4.47±0.90 mm/a. Taking the atmospheric data, ocean data and climate modal index from 1958 to 2020 as prediction factors, a variety of neural network models such as long short-term memory neural network model (LSTM model), recurrent neural network model (RNN model), gated recurrent unit neural network model (GRU model) and support vector machine regression model (SVR model) were established to predict the trend of relative sea level changes around the six tide gauge stations along the coast of China. The model evaluation results show that the average correlation coefficient and root mean square error of the observed value and the predicted value obtained by the LSTM model that simultaneously introduces atmospheric and ocean variables and climate modal index variables are 0.866 and 19.279 mm, respectively, which performs the best among the four models, and therefore the LSTM model can be used as a new method for predicting relative sea level changes.

Key words: sea level rise, relative sea level, absolute sea level, vertical land movement, satellite altimetry, tide level, LSTM neural network model, time series forecasting

中图分类号: