Land subsidence evolution and simulation in the western coastal area of Bohai Bay, China
C. Lu, L. Zhu, X. Li, H. Gong
College of Resource Environment and Tourism, Capital Normal University,
Beijing Key Laboratory of Resource Environment and Geographic Information System, Beijing, China
D. Du
China Geological Survey, North China Center for Geoscience Innovation Precambrian Research Center,
Tianjin, China
H. Wang
China Institute of Geo-Environment Monitoring, Beijing, China
P. Teatini
Dept. of Civil, Environmental and Architectural Engineering,
University of Padova, Padova, Italy
Groundwater overexploitation and loading of buildings have been the main factors triggering
land subsidence along the west coast of Bohai Bay, China, since the 2000s. Uneven subsidence
has been causing damage to buildings and civil facilities, loss of elevation, increasing the risk of
flood and seawater intrusion, and threatening the safety of people's lives and property. This paper
analyzed the spatial and temporal features of land subsidence along the coastal area from 2003
to 2010 and from 2015 to 2020, respectively. The relations between the initiating factors and land
subsidence were explored. Then, the simulation model of land subsidence was constructed through
a deep learning method. During the process, multiple data were collected, including land satellite
(Landsat), environmental satellite advanced synthetic aperture radar (ENVISAT ASAR) and Sentinel-1
images, leveling data, lithological data, and groundwater level data. The area occupied by buildings
and vertical displacement were extracted by using supervised classification, small baseline subset
(SBAS), and persistent scatterer interferometry (PSI) technologies. The gated recurrent unit (GRU)
neural network was adopted to simulate the evolution of land subsidence. Results showed that
the maximum annual vertical displacement rate decreased from -94 mm/yr during 2003-2010 to
-87 mm/yr during 2015-2020. The correlation efficiency between the groundwater level of the third
confined aquifer group and land subsidence was larger than the area occupied by buildings and
the compressible layer thickness with subsidence. The constructed GRU neural network model can
simulate subsidence from September 2019 to December 2019, with the overall RMSE and MAE being
3.16 mm and 2.19 mm, respectively. This work can facilitate an understanding of the evolution and
prevention of land subsidence along the west coast of Bohai Bay, which will provide information for
policy decisions and flood-fighting plans of the worldwide coastal cities.
