## 1 Introduction  
The provision of safe drinking water is not only an essential global health priority but also a major component of Sustainable Development Goal 6 of the United Nations. Although awareness of safe drinking water increased globally, many people still rely on unsafe water sources for consumption and daily use. As of 2022, it is estimated that approximately 1.7 billion people are using drinking water contaminated with feces, leading to horrible health consequences. Consumption of microbially contaminated water can lead to waterborne diseases such as diarrhea, cholera, typhoid, and hepatitis A. These diseases cause a huge number of deaths in the low-income world, with hundreds of preventable deaths each year [1]. The impact of this disease is especially high for children under five years of age. Thus, the availability of safe drinking water and its sustainable management are not only a public health need, but also a matter of social and economic concern. It is vital for preventing disease outbreaks and ensuring human health that the water quality parameters are tracked continuously and accurately. Traditional methods for water quality assessment include periodic laboratory analyses of physicochemical and microbiological parameters, which are time-consuming, labor-intensive, and costly. Complementarily, there has been a growing interest in computational prediction methods based on measurable water quality indicators. Machine learning (ML) techniques are potentially good candidates for classifying water potability automatically based on sensor data. Classical algorithms, such as decision trees, support vector machines (SVMs), k-nearest neighbors (KNN), and ensemble algorithms (random forest, AdaBoost, and XGBoost), have found great applications in this context. Among these, XGBoost emerged as a preferred method for handling large and heterogeneous datasets due to its capacity to manage missing values and model complex feature interactions. Empirical studies have shown that such tree-based models consistently outperform simpler classifiers such as logistic regression [2,3]. Nonetheless, conventional ML models often depend on manual feature engineering and may fail to capture the subtle nonlinear dependencies among water quality attributes. The above limitations are addressed by proposing deep learning as an alternative. It is a data-driven approach, where feature representations are learned automatically in an abstract manner. Nevertheless, it has been a point of contention as to whether classical multilayer perceptron's (MLPs) can outperform ensemble techniques on tabular datasets [4]. Thus, attempts to bridge this uncomfortable distance led to the conception of specialized neural architectures accommodating structured data. One of the most promising research directions in this context has been the exploration of attention mechanisms. For example, TabNet does sequential attention-based feature selection wherein the dynamic assignment of importance is given to the relevant features developed from each decision step to achieve high accuracy with interpretability [5]. Correspondingly, the TabTransformer adopts the self-attention approach characteristic of transformers to encode categorical variables for tabular data, which exhibited a performance level commensurate with state-of-the-art ensemble methods [6]. More recent transformer-based architectures, such as FTTransformer, embed each feature as a token and apply multi-head self-attention to model inter-feature relationships, thereby achieving or surpassing other deep learning approaches on structured prediction tasks [4]. SAINT extends this capability by introducing a dual-attention mechanism that models both feature-wise and row-wise dependencies. Moreover, with appropriate contrastive self-supervised pretraining, SAINT performed much better than the widely used ensemble methods XGBoost and LightGBM across different benchmarks [7].