## 4 Results and Discussion
### Validation  
Figure 8 presents nine representative time series of monthly groundwater level elevations recorded in situ (solid black) along with predictions from six deep learning architectures. The observed wells span three characteristic hydrogeologic regimes: (1) nearly flat plateaus in confined alluvial settings (e.g., Station 28 and Station 11), (2) slow, persistent seasonal drawdowns on fractured piedmonts (e.g., Station 20 and Station 47), and (3) strongly modulated responses in irrigated lowlands with double-peaked recharge signals (e.g., Station 23 and Station 6). All series exhibit pronounced minima during peak evapotranspiration each summer and sharp rises following major recharge events, providing a stringent testbed for model fidelity. Among the six predictors, the Transformer (yellow dash-dot) and ConvTransformer (teal dash-dot) consistently deliver the closest match to the black in situ curves across all nine wells. The pure Transformer captures both the long-term trends and the precise timing of seasonal troughs and rebounds, preserving amplitude without the phase lags or overshoot common to other methods. The ConvTransformer further refines this performance by reproducing short-term fluctuations and secondary peaks with minimal bias, even in the most dynamic lowland sites. In contrast, Informer (green dotted) tracks the broad strokes well but slightly underestimates extreme highs and lows; recurrent networks (BiLSTM and LSTM) drift in phase and amplitude over multi-year spans; and the CNN-LSTM hybrid smooths away much of the high-frequency variability. Together, these results highlight the superior skill of attention-only and convolution-augmented attention models--particularly Transformer and ConvTransformer--for operational groundwater monitoring. In the following, the plots exhibit monthly groundwater level observations (1520-1625 m) against six model predictions, with point density contours (Figure 9). Each panel shows the 1:1 line (grey dashed) and an OLS fit (solid black), whose slope, intercept, and R\({}^{2}\) measure bias and scaling errors. Recurrent models (BiLSTM, LSTM, and CNN-LSTM) exhibit substantial scatter and departures from unity:  
Figure 8: Contrasting simulated s with in situ observations: validation and temporal trends. The BiLSTM model produces the regression line with the least steep slope (y = 0.86\(\times\) + 220.86), low explanatory power (R\({}^{2}\) = 0.18), and largest spread (RMSE = 15.31 m, MAE = 10.68 m, MAPE = \(-\)0.5%), reflecting systematic under-prediction at the low end and over-prediction at the high end. CNN-LSTM nearly recovers unity (y = 0.96x + 59.04, R\({}^{2}\) = 0.44) and cuts RMSE to 12.45 m (MAE = 8.37 m, MAPE = \(-\)0.6%), but still shows clustered heteroscedastic deviations around the densest wells near 1560 m. LSTM improves R\({}^{2}\) to 0.59 (y = 0.87x + 201.59) and reduces RMSE further to 10.53 m (MAE = 7.16 m, MAPE = \(-\)0.7%), yet retains clear bimodal clouds corresponding to seasonal aquifer highs and lows. In contrast, attention-enhanced architectures concentrate tightly around the 1:1 line: ConvTransformer is the best linear performer (y = 0.99x + 10.78, R\({}^{2}\) = 0.83), delivering the smallest errors (RMSE = 6.15 m, MAE = 3.92 m, MAPE = \(-\)0.9%) and virtually no bias across the range. The transformer slightly over-predicts at depth (y = 1.10x \(-\) 155.39) but still achieves R\({}^{2}\) = 0.71 with RMSE = 8.76 m (MAE = 6.02 m, MAPE = \(-\)0.8%), indicating strong fidelity except for extreme highs. Informer balances both worlds (y = 0.89x + 174.07, R\({}^{2}\) = 0.40, RMSE = 13.31 m, MAE = 9.07 m, MAPE = \(-\)0.6%), with a compact density kernel that minimizes systematic drift while retaining robustness to non-stationary signals. There is a visible slight overestimation in high water level areas; the main reason is the lack of data in those regions that are located in northern training points.