10 citations as recorded by crossref.

1. Land Subsidence Simulation and Prediction in the Yellow River Delta Using Machine Learning Techniques T. Zhang et al. https://doi.org/10.1080/01490419.2025.2585076
2. A Hybrid Numerical Method Incorporating Machine Learning into Groundwater Level Model for Improving Simulation Accuracy L. Zhu et al. https://doi.org/10.1021/acsestwater.4c01276
3. Global assessment of land subsidence in 48 large coastal cities from a land cover perspective S. Zhang & N. Xu https://doi.org/10.1080/2150704X.2025.2511186
4. A predictive framework for land subsidence risk in Silakhor: integrating machine and deep learning A. Haghizadeh & Z. Hajizadeh https://doi.org/10.1007/s11356-026-37988-2
5. Temporal Analysis of Land Subsidence in DKI Jakarta Using the Long Short-Term Memory (LSTM) Model H. Fitriany et al. https://doi.org/10.53623/gisa.v5i2.880
6. Land subsidence prediction in Zhengzhou's main urban area using the GTWR and LSTM models combined with the Attention Mechanism Y. Yuan et al. https://doi.org/10.1016/j.scitotenv.2023.167482
7. Land subsidence simulation based on Extremely Randomized Trees combined with Monte Carlo algorithm Z. Wang et al. https://doi.org/10.1016/j.cageo.2023.105415
8. Forecasting surface movements based on PSI time series using machine learning algorithms N. Yagmur et al. https://doi.org/10.1080/01431161.2024.2331977
9. Surface deformation analysis and prediction driven by multimodal data, InSAR technology, and ARIMA-BP model in typical oil exploitation area F. Wang et al. https://doi.org/10.1080/01431161.2024.2443616
10. Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR H. Li et al. https://doi.org/10.1016/j.scitotenv.2021.149244