## 2 Related Work
Instead of an approach that considers only sample dependencies, SAINT models the dependencies across different instances within a batch by which the network learns across patterns dataset wide. Therefore, such complementary training is with contrastive pretraining and advanced embedding strategies, enabling it to consistently surpass baseline models across a variety of structured datasets. Accompanying architectural advances is another important research direction aimed at boosting generalization through unsupervised and self-supervised learning. [PERSON] et al. (2013) [17] joined [PERSON] et al. (2010) [18] in emphasizing that an unsupervised objective should allow pretraining of the deep neural networks--traditionally using objectives such as autoencoders or restricted Boltzmann machines--to learn an underlying structure of the data well within the time frame of supervised training. This line of development catalyzed recent transformer-based models such as TAPE, where autoencoder-style pretraining is performed together with attention-based encoders to develop robust feature representations from unlabeled data. The hybrid methods prove particularly useful, for example, in water quality assessment, where the availability of labeled datasets is limited or expensive. In recent times, innovations have become variegated concerning their adequacy for the assessment of water potability. For example, [PERSON] et al. (2024) [19] compared multiple ML algorithms, including decision tree, KNN, MLP, SVM, and random forest, showing that environmental monitoring tasks require interpretability, predictive performances, and reliable models, with random forest providing the best accuracy and F1 scores during their investigations. In [PERSON] et al. 's work (2024) [20], a hybrid deep learning model for water quality prediction in normal plain watershed environments was proposed, which combined LSTM, GRU, and Bayesian optimization. By improving the predictive performance of the algorithm, the framework proved to be promising by bringing down RMSE by 32.1% and enhancing R\({}^{2}\) over multiple targets, thus demonstrating its utility in heterogeneous water sources. The [PERSON] et al. (2024) [21] study is a comparative exercise between ANN and LSTM models for dissolved oxygen (DO) prediction in river waters. Their study established that although LSTM attained a lower MSE, superior results were seen in the case of the ANN model with respect to lower RMSE (0.00436) and MAPE (1.85%), indicating better overall accuracy in DO predictions. Classical neural architectures prove particularly effective in specific contexts that utilize well-calibrated model parametersand configurations tailored to domain-specific characteristics. As highlighted in his water quality assessments, [PERSON] et al. (2021) [22] presented evidence that the LightGBM model can effectively predict estuarine water levels in the Columbia River by considering upstream river discharges and tidal input. The model achieved a correlation coefficient ranging from 0.975 to 0.987 and RMSE values as low as 0.14 m, outperforming traditional physics-based models in both accuracy and skill score. The opposite development was carried out by [PERSON] and [PERSON] (2023) [23], who built a hybrid model based on CNN, BiLSTM, and the attention mechanism to obtain sequential dependencies in diverse water quality parameters. All these efforts thus manifest a considerable shift toward hybrid, interpretable, and context-aware models, addressing real-world problems along with their associated complexities in the estimation of water potability. A comprehensive tabular summary of the most recent scientific studies published between 2021 and 2024 on the estimation of drinking water potential using ML and deep learning (DL) techniques is presented in Table 1. The studies are arranged in reverse chronological order and provide a broad perspective in terms of the variety of algorithms used, types of datasets, and modeling approaches applied.