Ensemble Waves: Harnessing Bagging Classifiers for Crystal-Clear Water Quality Prediction

Sustaining life and improving public health need that clean water be made available. In proactive management of water resources and pollution reduction techniques, water quality parameters prediction is essential. We investigate a large range of water quality measures, including pH values, dissolved oxygen, turbidity, and nutrient concentrations, in several geographical regions. The bagging classifiers are a widely used machine learning method in this study work that aims to increase the accuracy and stability of a model. We project 76% percent accuracy after applying the bagging classifiers. This work uses a variety of approaches to demonstrate different learning strategies, including Bagging Classifiers, therefore advancing the quality of water. Our work provides insights into improving water quality assessment, which is essential for efficient environmental management and guaranteeing access to clean water for all, by using a variety of approaches, including ensemble learning.