InSAR time-series deformation forecasting surrounding Salt Lake using deep transformer models

被引:21
|
作者
Wang, Jing [1 ]
Li, Chao [1 ]
Li, Lu [1 ]
Huang, Zhihua [1 ]
Wang, Chao [2 ,3 ,4 ]
Zhang, Hong [2 ,3 ,4 ]
Zhang, Zhengjia [5 ]
机构
[1] Zhejiang Lab, Res Inst Intelligent Comp, Hangzhou 311121, Peoples R China
[2] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, 9 Dengzhuang South Rd, Beijing 100094, Peoples R China
[3] Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China
[4] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[5] China Univ Geosci, Fac Informat Engn, 388 Lumo Rd, Wuhan 430074, Peoples R China
基金
中国博士后科学基金;
关键词
InSAR; Qinghai -Tibet Plateau; Deformation prediction; Transformer; Salt Lake; Permafrost; TIBETAN PLATEAU; LAND SUBSIDENCE; SENTINEL-1; PREDICTION; INTERFEROMETRY; ALGORITHM; NETWORKS; OUTBURST; LEVEL;
D O I
10.1016/j.scitotenv.2022.159744
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The free and open data policy of Sentinel-1 SAR images enables Radar interferometry (InSAR) to perform time series surface deformation monitoring over large areas. InSAR deformation monitoring and prediction can investigate the freeze-thaw cycles of permafrost on the Qinghai-Tibet Plateau. However, the convolutional and recurrent neural net-works cannot accurately model long-term and complex relations in multivariate time series data, it is challenging to implement time series deformation prediction with high spatial resolution. In this paper, an innovative InSAR defor-mation prediction integrated algorithm based on the transformer models is proposed to predict time series deforma-tion more accurately surrounding Salt Lake. Compared with the other solutions, the unique feature of the proposed method is that: 1) this method takes advantage of the self-attention mechanism to study complicated dynamic defor-mation features of permafrost caused by temperature and other variables from InSAR time series deformation. 2) The transformer-based model can more accurately simulate seasonal and non-seasonal deformation signals, and is effective for short-term prediction of surface deformation in permafrost areas. The InSAR deformation prediction results dem-onstrate that the InSAR deformation prediction method achieves better prediction performance in predicting the de-formation trends of permafrost with a point scale compared with the prediction results of other models. Based on the predicted deformation and the water extraction results, the expansion trends surrounding Salt Lake are discussed and evaluated. The total area of Salt Lake increased by 57.32 km2 during the period 2015-2019. And Salt Lake maintained slowing expansion trend from 2019 to 2022. The time series deformation forecasting method can be used as a generic framework for modeling nonlinear deformation processes in complex permafrost areas, and it reveals the potential impact of the Salt Lake outburst event on the deformation processes and the degradation of permafrost.
引用
收藏
页数:17
相关论文
共 50 条
  • [41] FORECASTING WITH NON-LINEAR TIME-SERIES MODELS
    PEMBERTON, J
    STOCHASTIC PROCESSES AND THEIR APPLICATIONS, 1987, 26 (02) : 198 - 199
  • [42] FORECASTING PATIENT CENSUS - COMMONALITIES IN TIME-SERIES MODELS
    WOOD, SD
    HEALTH SERVICES RESEARCH, 1976, 11 (02) : 158 - 165
  • [43] BAYESIAN INFERENCES AND FORECASTING IN BILINEAR TIME-SERIES MODELS
    CHEN, CWS
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 1992, 21 (06) : 1725 - 1743
  • [44] TIME-SERIES FORECASTING USING FRACTIONAL DIFFERENCING
    SUTCLIFFE, A
    JOURNAL OF FORECASTING, 1994, 13 (04) : 383 - 393
  • [45] Traffic Forecasting using Time-Series Analysis
    Shuvo, Mohammmad Asifur Rahman
    Zubair, Muhtadi
    Purnota, Afsara Tahsin
    Hossain, Sarowar
    Hossain, Muhammad Iqbal
    PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTATION TECHNOLOGIES (ICICT 2021), 2021, : 269 - 274
  • [46] Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
    Zhou, Haoyi
    Zhang, Shanghang
    Peng, Jieqi
    Zhang, Shuai
    Li, Jianxin
    Xiong, Hui
    Zhang, Wancai
    THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2021, 35 : 11106 - 11115
  • [47] TIME-SERIES FORECASTING USING ROBUST REGRESSION
    LEVENBACH, H
    JOURNAL OF FORECASTING, 1982, 1 (03) : 241 - 255
  • [48] Take an Irregular Route: Enhance the Decoder of Time-Series Forecasting Transformer
    Shen, Li
    Wei, Yuning
    Wang, Yangzhu
    Qiu, Huaxin
    IEEE INTERNET OF THINGS JOURNAL, 2024, 11 (08): : 14344 - 14356
  • [49] Incorporation of Coordinate−Time Function(CT−PIM)time-series InSAR deformation prediction for salt mining areas:Case study of the Huaian Salt Mine
    Zhang T.
    Xing X.
    Peng W.
    Zhu J.
    Liu X.
    Ge J.
    Lei M.
    National Remote Sensing Bulletin, 2024, 28 (06) : 1615 - 1631
  • [50] Forecasting of Water Quality for the River Ganga using Univariate Time-series Models
    Kogekar, Aishwarya Premlal
    Nayak, Rashmiranjan
    Pati, Umesh Chandra
    2021 8TH INTERNATIONAL CONFERENCE ON SMART COMPUTING AND COMMUNICATIONS (ICSCC), 2021, : 52 - 57