海洋学研究 ›› 2022, Vol. 40 ›› Issue (2): 53-61.DOI: 10.3969-j.issn.1001-909X.2022.02.006

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基于ARIMA与LSTM的海岸带地面沉降预测方法

  

  1. 1.上海交通大学海洋学院，上海 200240；
    2.自然资源部海底科学重点实验室，浙江 杭州 310012； 3.自然资源部第二海洋研究所，浙江 杭州 310012；
    4.自然资源部海洋空间资源管理技术重点实验室，浙江 杭州 310012；
    5.浙江省海洋科学院，浙江 杭州 310012
  • 出版日期:2022-06-15 发布日期:2022-06-15

ARIMA-and LSTM-based forecasting method of land subsidence in coastal zone: A case study from the Hangzhou Bay and its adjacent area

  1. 1.School of Oceanography, Shanghai Jiao Tong University, Shanghai 200240, China; 2.Key Laboratory of Submarine Geosciences, MNR, Hangzhou 310012, China; 3.Second Institute of Oceanography, MNR, Hangzhou 310012, China; 4.Key Laboratory of Ocean Space Resource Management Technology, MNR, Hangzhou 310012, China; 5.Marine Academy of Zhejiang Province, Hangzhou 310012, China
  • Online:2022-06-15 Published:2022-06-15

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摘要： 快速的地面沉降是一种地质灾害，它关系到社会的可持续发展，甚至威胁人类的生命财产安全。InSAR技术可以获取地表长时间、大范围的形变数据，可用于分析潜在的地面沉降问题，为预防地质灾害提供了一种可靠手段。如何基于InSAR数据对地面沉降进行预测，一直是研究人员关注的重点方向和难题。为此，本文在前人对地面沉降预测研究的基础上，提出了一种将差分移动平均自回归(ARIMA)模型与深度学习中的长短期记忆单元(LSTM)模型相结合的地面沉降预测方法，即利用InSAR得到的形变量数据与ARIMA模型预测结果作差，然后利用LSTM对该差值进行训练与预测。以杭州湾2017—2019年InSAR监测数据为例验证了该方法，结果表明，与传统的单一预测算法相比，本文方法的均方根误差至少减小了2.23 mm，平均绝对误差至少减小了0.98 mm，平均预测精度至少提升了15.19%，验证结果证实了本文方法的可行性，为地面沉降预警工作提供了新的思路和方法。

关键词: 地面沉降, InSAR, LSTM, ARIMA, 海岸带

Abstract: Rapid land subsidence is a kind of geological disaster, which is related to the sustainable development of society and even threatens the safety of human life and property. InSAR technology can obtain long-term and large-scale surface deformation data, analyze potential land subsidence problems, and then provide reliable means for preventing geological disasters. How to predict land subsidence based on InSAR data has always been the key direction and problem that researchers focus on. Thus, on the basis of previous research on land subsidence prediction, a land subsidence prediction method was proposed, which combines the Auto Regressive Integrated Moving Average (ARIMA) model with the Long Short-Term Memory (LSTM) model in deep learning. The difference between the deformation data and the prediction result of the ARIMA model was obtained and then LSTM was used to train and predict the difference. Taking the InSAR monitoring data of Hangzhou Bay from 2017 to 2019 as an example, the method was verified. The result shows that compared with the traditional single prediction algorithm, the root mean square error of the method is reduced by at least 2.23 mm, and the mean absolute error is reduced by at least 0.98 mm, and the average prediction accuracy is improved by at least 15.19%, which verifies the feasibility of this method and provides some ideas and methods for early warning of ground subsidence.

Key words: land subsidence, InSAR, LSTM, ARIMA, coastal zone