سابِق|تال

Land subsidence simulation in the east of Beijing plain based on the AM-LSTM Network

  • role： First author第一作者
  • Affiliation：

    College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China

    Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China

    Laboratory Cultivation Base of Environment Process and Digital Simulation, Capital Normal University, Beijing 100048, China

  • Email：rainxinyucao@163.com
  • Introduction：曹鑫宇，研究方向为地表形变监测。E-mail： rainxinyucao@163.com
CAO Xinyu123,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China

    Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China

    Laboratory Cultivation Base of Environment Process and Digital Simulation, Capital Normal University, Beijing 100048, China

    Key Laboratory of Mechanism, Prevention and Mitigation of Land Subsidence, Capital Normal University, Beijing 100048, China

    Observation and Research Station of Groundwater and Land Subsidence in Beijing-Tianjin-Hebei Plain, MNR, Beijing 100081, China

  • Email：lin.zhu@cnu.edu.cn
  • Introduction：朱琳，研究方向为信息水文地质。E-mail： lin.zhu@cnu.edu.cn
ZHU Lin12347*,  
  • Affiliation：

    College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China

    Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China

    Laboratory Cultivation Base of Environment Process and Digital Simulation, Capital Normal University, Beijing 100048, China

    Key Laboratory of Mechanism, Prevention and Mitigation of Land Subsidence, Capital Normal University, Beijing 100048, China

    Observation and Research Station of Groundwater and Land Subsidence in Beijing-Tianjin-Hebei Plain, MNR, Beijing 100081, China

GONG Huili12347,  
  • Affiliation：

    College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China

    Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China

    Laboratory Cultivation Base of Environment Process and Digital Simulation, Capital Normal University, Beijing 100048, China

    Key Laboratory of Mechanism, Prevention and Mitigation of Land Subsidence, Capital Normal University, Beijing 100048, China

    Observation and Research Station of Groundwater and Land Subsidence in Beijing-Tianjin-Hebei Plain, MNR, Beijing 100081, China

GUO Lin12347,  
  • Affiliation：

    College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China

    Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China

    Laboratory Cultivation Base of Environment Process and Digital Simulation, Capital Normal University, Beijing 100048, China

WEI Yujiao123,  
  • Affiliation：

    Institute of Remote Sensing and Digital Agriculture, Sichuan Academy of Agricultural Sciences, Chengdu 610066, China

GUO Tao5,  
  • Affiliation：

    College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China

    Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China

    Laboratory Cultivation Base of Environment Process and Digital Simulation, Capital Normal University, Beijing 100048, China

    Key Laboratory of Mechanism, Prevention and Mitigation of Land Subsidence, Capital Normal University, Beijing 100048, China

CHEN Beibei1234,  
  • Affiliation：

    Institute of Geological Environment Monitoring, China Geological Survey, Beijing 100081, China

    Observation and Research Station of Groundwater and Land Subsidence in Beijing-Tianjin-Hebei Plain, MNR, Beijing 100081, China

WANG Haigang67,  
  • Affiliation：

    College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China

    Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China

    Laboratory Cultivation Base of Environment Process and Digital Simulation, Capital Normal University, Beijing 100048, China

LI Huijun123

ملخص

The simulation and prediction model of land subsidence based on traditional numerical methods requires a large amount of hydrogeological and measured data, and predicting the deformation in areas with complex geological conditions is difficult. In this study, on the basis of land subsidence information obtained by permanent scatterers–interferometry synthetic aperture radar (PS-InSAR) technology in the east of the Beijing plain and in consideration of the influence of groundwater level in different layers on subsidence, the long-term and short-term memory network (AM-LSTM) based on an attention mechanism is used to simulate the land subsidence at typical locations in different subsidence areas. Results show the following points. (1) The spatial difference of land subsidence in the study area is obvious. From October 2010 to August 2016, the maximum subsidence rate is about 153 mm/a, and the cumulative subsidence is 1063 mm. The area is located near Sanjianfang Township in Chaoyang District. (2) The simulation accuracy of the AM-LSTM model is better than that of the traditional LSTM model, and the accuracy of this simulation reaches 22%. (3) The attention weight of the AM-LSTM model indicates that the water level of the second confined aquifer contributes the most to land subsidence. These research findings can provide a reliable model for the prevention and control of land subsidence.

مفهوم

remote sensing;land subsidence;AM-LSTM;simulation and prediction;groundwater level of different layers;attention weight

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