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

文献类型： 外文期刊

第一作者： Li, Huijun

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

作者机构：

关键词： Land subsidence; Spatiotemporal modeling; GW-LSTM; PS-InSAR; Uncertainty analysis

期刊名称：SCIENCE OF THE TOTAL ENVIRONMENT （ 影响因子：10.753； 五年影响因子：10.237 ）

ISSN： 0048-9697

年卷期： 2021 年 799 卷

页码：

收录情况： SCI

摘要： 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. (c) 2021 Elsevier B.V. All rights reserved.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序