Purity Unveiled: The Power of Ensemble Bagging in Water Quality

Ensuring access to clean water is crucial for maintaining public health. Effective management of water resources and the reduction of pollution rely heavily on accurate predictions of water quality. Water quality can be assessed through various indicators, such as pH levels, dissolved oxygen, water clarity, and nutrient concentrations, which vary across different locations. Bagging classifiers, a widely used machine learning technique, are employed to enhance the accuracy and stability of predictive models for water quality. With the application of bagging classifiers, an accuracy of approximately 76% is anticipated. This approach demonstrates the effectiveness of ensemble learning methods in improving water quality prediction models. The proposed methods aim to optimize the assessment process for water quality, contributing to better environmental management and ensuring the availability of clean water for all communities.