Spatial-temporal evolution characteristics and prediction of land subsidence in the eastern plain of Beijing

doi:10.6046/zrzyyg.2021390

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Abstract

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

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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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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021390 OR https://www.gtzyyg.com/EN/Y2022/V34/I4/183

Fig.1 Overview of the study area

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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