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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (4) : 183-193     DOI: 10.6046/zrzyyg.2021390
Spatial-temporal evolution characteristics and prediction of land subsidence in the eastern plain of Beijing
YU Wen1,2,3,4(), GONG Huili1,2,3,4(), CHEN Beibei1,2,3,4, ZHOU Chaofan1,2,3,4
1. Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China
2. Key Laboratory of Land Subsidence Mechanism and Control, Ministry of Education, Capital Normal University, Beijing 100048, China
3. Base of the State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China
4. National Field Scientific Observation and Research Station of Groundwater and Land Subsidence in the Beijing-Tianjin-Hebei Plain, Capital Normal University, Beijing 100048, China
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Land subsidence is a natural geological phenomenon in which the surface elevation drops. It can severely destroy urban infrastructure and threaten urban safety if it occurs in densely populated cities with a high social development degree. The analysis of the evolution characteristics of land subsidence can reflect the degree of the influence of land subsidence on the ground infrastructures, and building an efficient land subsidence prediction model is of great significance for preventing and controlling land subsidence and protecting urban safety. This study obtained the spatial-temporal information on land subsidence using the persistent scatterer interferometric synthetic aperture Radar (PS-InSAR) method first and then verified the information using leveling to get high precision. Then, this study analyzed the general spatial-temporal characteristics of the land subsidence field using an empirical orthogonal function. The analysis results are as follows. Spatial modal No. 1 had a high variance contribution rate, almost representing the general spatial evolution of the study area. Its corresponding time coefficient showed a significant linear trend. By contrast, spatial mode No. 2 had a low variance contribution rate and a seasonally significant time coefficient. Finally, the time series of the regional land subsidence were predicted using both long short-term memory (LSTM) and Attention-LSTM models. The prediction results indicate that the Attention-LSTM model was superior to the LSTM model, with the mean square error loss (MSE-loss) of as low as 0.01. This prediction method expands the application of deep learning in the study of land subsidence.

Keywords land subsidence      empirical orthogonal function      evolution characteristics      Attention-LSTM      time series prediction     
ZTFLH:  TP79  
Issue Date: 27 December 2022
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Wen YU
Huili GONG
Beibei CHEN
Chaofan ZHOU
Cite this article:   
Wen YU,Huili GONG,Beibei CHEN, et al. Spatial-temporal evolution characteristics and prediction of land subsidence in the eastern plain of Beijing[J]. Remote Sensing for Natural Resources, 2022, 34(4): 183-193.
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Fig.1  Overview of the study area
雷达影像参数 RADARSAT-2 Sentinel-1
轨道方向 降轨 升轨
空间分辨率/m 30 5×20
波段 C波段 C波段
极化方式 VV VV
波长/cm 5.6 5.6
重访周期/d 25 12
影像数量/景 48 61
Tab.1  S1A Radar image information
Fig.2  Schematic diagram of LSTM unit structure
Fig.3  Land subsidence prediction framework using Attention-LSTM
Fig.4  Cumulative settlement of the study area
Fig.5  Time series annual settlement map of the study area
Fig.6  Verification of InSAR results and leveling results
Fig.7  Distribution of subsidence fecture vectors and corresponding time coefficients of typical subsidence areas in the eastern plain of Beijing from 2011 to 2018
Fig.8  Distribution of monthly-scale subsidence feature vectors and corresponding time coefficients of typical subsidence area in the eastern plain of Beijing from 2012 to 2014
Fig.9  Distribution of monthly-scale subsidence feature vectors and corresponding time coefficients of typical subsidence area in the eastern plain of Beijing from 2016 to 2018
Fig.10  Forecast model loss function
Fig.11  Regional settlement prediction results of LSTM and Attention-LSTM
Fig.12  Comparison between the real value and the predicted value of the selected profile in the study area
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