Published Paper Details:

Ensuring clean and safe water is vital for human health, environmental balance, and sustainable development. Increasing pollution caused by urbanization, industrial discharge, and agricultural runoff has made water quality monitoring a critical challenge. Traditional assessment techniques rely on manual sampling and laboratory testing, which are often time-consuming, expensive, and unsuitable for continuous monitoring. To address these limitations, this study presents a machine learning-based approach for water quality prediction and classification using physicochemical parameters. The proposed framework utilizes key indicators such as pH, turbidity, temperature, dissolved oxygen, electrical conductivity, and total dissolved solids to evaluate water conditions. Data pre-processing and feature analysis are applied to enhance model reliability, followed by the implementation of supervised learning algorithms for prediction and classification of water samples into safe and unsafe categories. Experimental results demonstrate that the machine learning models achieve accurate and consistent performance, proving their effectiveness over conventional methods. The study highlights the potential of intelligent data-driven systems for real-time water quality monitoring, early pollution detection, and sustainable water resource management.