LAND SUBSIDENCE SUSCEPTIBILITY MAPPING USING MACHINE LEARNING ALGORITHMS

14 citations as recorded by crossref.

1. Accuracy enhancement in land subsidence prediction using lightgbm and metaheuristic optimization M. Shirali et al. https://doi.org/10.1007/s12145-025-01929-3
2. Integrating Machine Learning with Metaheuristic Optimization for Accurate and Interpretable Land Subsidence Susceptibility Mapping in Semi-Arid Regions M. Shirali et al. https://doi.org/10.1007/s41748-025-00659-2
3. Land subsidence susceptibility mapping in urban settlements: Integrating persistent scatterer interferometry with a comparative machine learning approach S. Hussain et al. https://doi.org/10.1016/j.pce.2026.104393
4. Challenges in comparing land subsidence measurements by PS-InSAR with simulations from coupled hydro-geomechanical modelling: a case study in Antwerp Harbour A. Choopani et al. https://doi.org/10.1007/s10040-025-02970-8
5. 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
6. Optimizing spatial prediction of land subsidence through synergized non-parametric machine learning and swarm-based metaheuristic algorithms S. Razavi-Termeh et al. https://doi.org/10.1007/s10651-026-00704-x
7. Virtual reality-assisted visualization of flood susceptibility using optimized machine learning models S. Razavi-Termeh et al. https://doi.org/10.1007/s13201-025-02606-y
8. Hydrological and tectonic linkage to subsidence risk in the Rhineland coalfields, Germany: Integrating machine learning with remote sensing and geospatial data D. Ritushree et al. https://doi.org/10.1016/j.enggeo.2026.108751
9. Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran H. Gharechaee et al. https://doi.org/10.3390/land12040843
10. Evaluating Machine Learning-Based Approaches in Land Subsidence Susceptibility Mapping E. Hosseinzadeh et al. https://doi.org/10.3390/land13030322
11. Spatio-temporal modeling of asthma-prone areas: Exploring the influence of urban climate factors with explainable artificial intelligence (XAI) S. Razavi-Termeh et al. https://doi.org/10.1016/j.scs.2024.105889
12. Enhancing land subsidence susceptibility mapping using deep tabular learning optimization with metaheuristic algorithms S. Razavi-Termeh et al. https://doi.org/10.1016/j.gr.2025.07.002
13. Improving the performance of evidential belief function model using five machine learning models in earth fissure susceptibility mapping X. Chen et al. https://doi.org/10.1016/j.asr.2024.09.054
14. PS-InSAR and Explainable Machine Learning for Local Transportation Infrastructure Stability in Baton Rouge A. Abdalla et al. https://doi.org/10.1109/JSTARS.2026.3705283