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
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10040-024-02771-5.pdf
Reference91 articles.
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2. Al Mukaimi ME, Dellapenna TM, Williams JR (2018) Enhanced land subsidence in Galveston Bay, Texas: interaction between sediment accumulation rates and relative sea level rise. Estuar Coast Shelf Sci 207:183–193
3. Arabameri A, Chandra Pal S, Rezaie F, Chakrabortty R, Chowdhuri I, Blaschke T, Thi Ngo PT (2021a) Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation. J Environ Manage 284:112067. https://doi.org/10.1016/j.jenvman.2021.112067
4. Arabameri A, Santosh M, Rezaie F, Saha S, Coastache R, Roy J, Mukherjee K, Tiefenbacher J, moayedi H (2021b) Application of novel ensemble models and k-fold CV approaches for Land subsidence susceptibility modelling. Stoch Environ Res Risk Assess 7. https://doi.org/10.1007/s00477-021-02036-7
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