## 4 Results and Discussion
### Implications and Applications  
The superior performance of transformer-based architectures, particularly the ETT and SAINT, demonstrates the efficacy of attention mechanisms in capturing complex relationships between water quality parameters. ETT, in particular, offers to achieve 95.04%  
\begin{table}
\begin{tabular}{l c c c c c c c c c}  & & & & & & & & & & \\ \hline count & 2011.0 & 2011.0 & 2011.0 & 2011.0 & 2011.0 & 2011.0 & 2011.0 & 2011.0 & 2011.0 \\ mean & 7.09 & 95.97 & 21,917.44 & 7.13 & 333.22 & 426.53 & 14.36 & 66.40 & 3.97 \\ std & 1.57 & 32.64 & 8642.24 & 1.58 & 41.26 & 80.71 & 3.32 & 16.08 & 0.78 \\ min & 0.23 & 73.49 & 320.94 & 1.39 & 129.00 & 201.62 & 2.20 & 8.58 & 1.45 \\
25\% & 6.09 & 176.75 & 15,615.67 & 6.14 & 307.63 & 366.68 & 12.12 & 55.95 & 3.44 \\
50\% & 7.03 & 197.19 & 20,933.51 & 7.14 & 332.23 & 423.46 & 14.32 & 66.54 & 3.97 \\
75\% & 8.05 & 216.44 & 27,182.59 & 8.11 & 359.33 & 482.37 & 16.68 & 77.29 & 4.51 \\ max & 14.00 & 317.34 & 56,488.67 & 13.13 & 481.03 & 753.34 & 27.01 & 124.00 & 6.49 \\ \hline \end{tabular}
\end{table}
Table 7: Data statistics of the water_potability dataset.  
of predictive accuracy, computational efficiency, and interpretability, making it suitable for deployment in diverse settings where comprehensive laboratory testing is challenging. Its ability to highlight key physicochemical parameters such as chloramines and conductivity can inform the design of targeted sensor arrays, reducing the complexity and cost of monitoring infrastructure. SAINT's dual-attention mechanism, while computationally intensive, provides a unique advantage in capturing population-level patterns, which could be leveraged in large-scale environmental studies involving multiple water sources.  
TAPE's efficiency and competitive accuracy make it an appealing choice for scenarios with limited computational resources or unlabeled data, such as preliminary water quality assessments in developing regions. However, its lower F1 score suggests that further optimization, potentially through hybrid pretraining strategies, is needed to match ETT's precision. Although FTTransformer is theoretically promising, its relatively poor performance in this study highlights the difficulty of applying transformer models to tabular data without careful hyperparameter tuning. This finding aligns with prior research indicating that transformer models can be sensitive to dataset characteristics and tuning [4].  
The feature importance analysis provides valuable insights for practical applications in water quality monitoring. The identification of chloramines, pH, and sulfate as highly influential parameters across multiple models suggests these could serve as priority indicators in rapid screening protocols. This could inform the development of more targeted and cost-effective monitoring approaches, particularly in regions where comprehensive water quality assessment resources are limited.  
Comparative analysis using feature subsets further supports this application potential, demonstrating that even with reduced parameter sets, transformer-based models can maintain reasonable classification accuracy. Configuration 5* (pH, hardness, chloramines, and sulfate) yielded the highest accuracy among all subset configurations across models, suggesting this combination of parameters might offer an optimal balance between measurement simplicity and predictive power for field deployment scenarios.