ABSTRACT Land subsidence is an increasing environmental hazard in semi‐arid agricultural basins where intensive groundwater abstraction, compressible geological units, and expanding land‐use pressures interact. This study presents a PS‐InSAR and machine‐learning‐based framework for land subsidence susceptibility mapping in the Çumra District of the Konya Closed Basin, Türkiye. A time‐series PS‐InSAR analysis was conducted using 172 Sentinel‐1A ascending images acquired between 2018 and 2023, and 11,718 high‐confidence subsidence points were extracted as the inventory dataset. Fourteen conditioning factors representing topographic, hydrogeological, lithological, environmental, and anthropogenic controls were integrated with eight tree‐based ensemble models optimized using Optuna. Random Forest achieved the highest overall performance, with an accuracy of 0.9745, precision of 0.9562, recall of 0.9945, F1 score of 0.9750, and Cohen's Kappa of 0.9490, followed by Extremely Randomized Trees and XGBoost. Class‐specific validation also confirmed the reliability of Random Forest in delineating the Very High Susceptibility class. Variable importance analysis identified land use, proximity to settlements, well density, and groundwater level change as the dominant predictors, emphasizing the role of groundwater‐related anthropogenic pressure in subsidence development. The resulting susceptibility maps provide a spatial decision‐support basis for groundwater management, land use planning, and subsidence risk mitigation.
Burhan Baha Bilgilioğlu (Mon,) studied this question.