Identification of the correlation between land subsidence and groundwater level in Cangzhou, North China Plain, based on time-series PS-InSAR and machine-learning approaches

• Published in: Hydrogeology journal Vol. 32; no. 4; pp. 951 - 966
• Main Authors: Nafouanti, Mouigni Baraka, Li, Junxia, Li, Hexue, Ngata, Mbega Ramadhani, Sun, Danyang, Huang, Yihong, Zhou, Chuanfu, Wang, Lu, Nyakilla, Edwin E.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2024; Springer Nature B.V
• Subjects: Aquatic Pollution; Aquifers; artificial intelligence; China; Deformation; Earth and Environmental Science; Earth Sciences; evolution; Geology; Geophysics/Geodesy; Groundwater; Groundwater levels; Hydrogeology; Hydrology/Water Resources; Identification methods; Interferometric synthetic aperture radar; Interferometry; Land subsidence; Learning algorithms; Machine learning; Overexploitation; Radar; SAR (radar); subsidence; Synthetic aperture radar; Synthetic aperture radar interferometry; Time series; time series analysis; Waste Water Technology; Water diversion; Water Management; Water Pollution Control; Water Quality/Water Pollution; water table; China; PS-InSAR; Random forest; k-nearest neighbor; China; Groundwater level

Land deformation is a severe environmental problem that is often caused by groundwater overexploitation. Traditional approaches, such as those based on ground leveling, are used as standard for monitoring land deformation, but they cannot collect enough information for land-deformation mapping. In t...