Other ensemble approaches are also widely applied in safe drinking water supply such as RF , XGB , boosted decision trees (BDT) , and stacking model . The core strength of these ensemble techniques is their ability to amalgamate predictions from several models, aiming to boost accuracy and diminish overfitting . In drinking water research where data can be noisy, varied, and sometimes sparse, such strategies are invaluable. Several comparative studies have delved into the performance nuances of different ensemble models. A recurring observation in these investigations is that the slight edge XGB outperforms RF . Furthermore, BRT outperforms RF . Interestingly, while XGB has consistent prowess, LightGBM, another gradient boosting framework, outperforms XGB . Therefore, as gradient boosting algorithms continue to evolve, newer iterations such as LightGBM offers even more refined performance. However, while ensemble methods offer certain advantages, their efficacy is not universally dominant across scenarios. The best model is often contingent upon the nature of the problem, the characteristics of the data, and the specific objectives of the study. Ensemble models, with their ability to amalgamate insights from multiple "weak learners," might excel in scenarios where data are diverse, noisy, and/or sparse . By contrast, for problems where the data structures are deeply hierarchical or when data patterns are straightforward, NNbased models or regression-based models are more suitable. The crucial factor is to match the ability of the models with the specific demands and characteristics of the data sets.