Spatiotemporal modeling of land subsidence using a geographically
weighted deep learning method based on PS-InSAR
H. Li, L. Zhu, H. Gong
Capital Normal University, Beijing, China
Z. Dai
College of Construction Engineering, Jilin University, Changchun, China
T. Guo
Institute of Remote Sensing and Digital Agriculture, Sichuan Academy of Agricultural Sciences, Chengdu, China
G. Guo
Beijing Institute of Hydrogeology and Engineering Geology, Beijing, China
J. Wang
National Computational Infrastructure, Australian National University, Canberra, Australia
P. Teatini
Dept. of Civil, Environmental and Architectural Engineering,
University of Padova, Padova, Italy
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 performwell 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 modelwhich 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 SCwith 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.
