## 4 Results and Discussion
### Comparative Analysis and Implications  
This study advances groundwater level prediction by integrating remote sensing-derived land subsidence, spectral indices, and hydroclimatic variables within a ConvTransformer framework. By synthesizing insights from [PERSON] et al. [19], [PERSON] et al. [2], and [PERSON] et al. [48], we contextualize our contributions within the evolving landscape of spatiotemporal groundwater modeling. Our study addresses critical gaps in localized aquifer monitoring, particularly for culturally significant regions, while building upon recent methodological advancements. This discussion explores the evolution of predictive techniques, the unique integration of high-resolution data for heritage preservation, and the implications for sustainable resource management in semi-arid zones. [PERSON] et al. [19] illustrated the utility of GRACE-derived terrestrial water storage (TWS) and SAR-based deformation data for predicting groundwater storage (GWS) in Shanxi, China, using LSTM networks to capture large-scale temporal trends linked to climate variability. In contrast, our study integrates SBAS-InSAR-derived subsidence at 30 m resolution, enabling localized detection of aquifer stressors near UNESCO World Heritage  
Figure 14: Annual predictions. Sites such as Persepolis and Naqsh-e Rustam. While GRACE provides valuable basin-scale TWS estimates, its coarse resolution (\(\sim\)1\({}^{\circ}\)) limits its applicability for heritage conservation. Our high-resolution subsidence feedback loops address this gap, extending the study of [PERSON] et al. [33] on InSAR-based aquifer compaction monitoring in Iran to predictive modeling for socio-environmental stewardship. [PERSON] et al. [2] validated the Transformer's superiority over CNNs, LSTMs, and hybrid models for groundwater level prediction in Taiwan, emphasizing self-attention mechanisms for capturing long-term dependencies. Our ConvTransformer framework, combining spatial convolution with temporal attention, achieves comparable accuracy (R\({}^{2}\approx 0.83\)) while explicitly modeling land subsidence feedbacks. Unlike [PERSON] et al. 's station-based approach, our grid-level predictions reveal heterogeneous patterns driven by agricultural pumping and karst geology in the Marvdasht Plain. This spatially distributed modeling aligns with [PERSON] et al. 's [12] recommendations for complex aquifers, particularly in data-scar regions. Additionally, our use of attention weights to quantify uncertainty in high-prediction-error zones (e.g., fault-controlled aquifers) advances traditional LUBE methods [69], offering a dynamic framework for risk assessment. [PERSON] et al. [48] conducted a multi-scale analysis of groundwater storage dynamics across North African basins, leveraging GRACE data, downscaled meteorological products, and land surface models to assess climate-driven variability. Their focus on large-scale climate teleconnections (e.g., North Atlantic Oscillation) contrasts with our high-resolution (30 m) predictions, which fuse SBAS-InSAR-derived subsidence with spectral and hydroclimatic covariates to capture anthropogenic stressors like agricultural pumping. While [PERSON] et al. [48] emphasized basin-scale climatic linkages, our approach bridges groundwater dynamics with surface deformation, addressing localized extraction impacts--a limitation of GRACE noted by [PERSON] et al. [70]. A key strength of our study lies in its applicability to data-scarce heritage regions. [PERSON] et al. [48] highlighted remote sensing's role in filling observational gaps in North Africa through GRACE downscaling and ERA5-Land reanalysis. Our framework complements this by integrating Sentinel-1 InSAR, Landsat NDWI, and CHIRPS precipitation data, providing a scalable solution for regions lacking dense hydrological networks, as advocated by [PERSON] et al. [70]. However, similar to [PERSON] et al.