Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR

Huijun Li; Lin Zhu; Zhenxue Dai; Huili Gong; Tao Guo; Gaoxuan Guo; Jingbo Wang; Pietro Teatini

doi:10.1016/j.scitotenv.2021.149244

Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR

Sci Total Environ. 2021 Dec 10:799:149244. doi: 10.1016/j.scitotenv.2021.149244. Epub 2021 Jul 24.

Authors

Huijun Li¹, Lin Zhu², Zhenxue Dai³, Huili Gong¹, Tao Guo⁴, Gaoxuan Guo⁵, Jingbo Wang⁶, Pietro Teatini⁷

Affiliations

¹ Laboratory Cultivation Base of Environment Process and Digital Simulation, Beijing Laboratory of Water Resources Security, Key Laboratory of 3-Dimensional Information Acquisition and Application, Capital Normal University, Beijing 100048, China.
² Laboratory Cultivation Base of Environment Process and Digital Simulation, Beijing Laboratory of Water Resources Security, Key Laboratory of 3-Dimensional Information Acquisition and Application, Capital Normal University, Beijing 100048, China. Electronic address: 5533@cnu.edu.cn.
³ College of Construction Engineering, Jilin University, Changchun 130026, China.
⁴ Institute of Remote Sensing and Digital Agriculture, Sichuan Academy of Agricultural Sciences, Chengdu 610066, China.
⁵ Beijing Institute of Hydrogeology and Engineering Geology, Beijing 100048, China.
⁶ National Computational Infrastructure, Australian National University, Canberra, Australia.
⁷ Dept. of Civil, Environmental and Architectural Engineering, University of Padova, Padova 35121, Italy; UNESCO-LaSII (Land Subsidence International Initiative), Querétaro, Mexico.

PMID: 34365261
DOI: 10.1016/j.scitotenv.2021.149244

Abstract

The demand for water resources during urbanization forces the continuous exploitation of groundwater, resulting in dramatic piezometric drawdown and inducing regional land subsidence (LS). This has greatly threatened sustainable development in the long run. LS modeling helps understanding the factors responsible for the ongoing loss of land elevation and hence enhances the development of prevention strategies. Data-driven LS models perform well with fewer variables and faster convergence than physically-based hydrogeological models. However, the former models often cannot simultaneously reflect the temporal nonlinearity and spatial correlation (SC) characteristics of LS under complex variables. We proposed a LS spatiotemporal model which considers both nonlinear and spatial correlations between LS and groundwater level change of exploited aquifers. It is based on deep learning method and LS time series detected by permanent scatterer-interferometric synthetic aperture radar (PS-InSAR). The LS time series and hydrogeological properties are constructed as a spatiotemporal dataset for model training. The spatiotemporal LS model, geographically weighted long short-term memory (GW-LSTM), is constructed by integrating SC with LSTM. This latter is a deep recurrent neural network approach incorporating sequential data. The model is validated by a case study in the Beijing plain. The results show that the accuracy of the proposed model can be greatly improved considering the spatial correlation between LS and influencing factors. Furthermore, the comparison between the LSTM and GW-LSTM models reveals that groundwater level variation is not a unique causation of LS in the study area. The developed model deals with the spatiotemporal characteristics of LS under multiple variables and can be used to predict LS under different scenarios of groundwater level variations for the purpose of monitoring and providing evidence to support the prevention of future LS.

Keywords: GW-LSTM; Land subsidence; PS-InSAR; Spatiotemporal modeling; Uncertainty analysis.

MeSH terms

Beijing
Deep Learning*
Groundwater*
Neural Networks, Computer
Radar