Water Quality Prediction And Classification Using Machine Learning

Water quality assessment plays a vital role in environmental monitoring, public health, and sustainable water resource management.It helps in monitoring the health of water bodies, ensuring that they meet safety standards for human consumption and other uses.By regularly assessing water quality, we can detect contaminants, prevent waterborne diseases, and manage water resources effectively.Traditional water testing methods are often time-consuming, expensive, and require laboratory expertise.This study focuses on developing a data-driven approach for predicting and classifying water quality based on various physicochemical parameters such as PH, Turbidity, Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), and Total Dissolved Solids (TDS).By leveraging advanced data analytics and classification techniques, this research aims to provide an efficient and automated solution for water quality assessment.Various machine learning algorithms, including Decision Trees, Random Forest, Support Vector Machines (SVM), are employed to analyze historical water quality data.The models are evaluated based on their accuracy, precision, and recall, ensuring robust performance.The findings of this study indicate that machine learning-based classification models can significantly improve water quality monitoring by providing real-time predictions and early warnings for contamination risks.This approach facilitates informed decision-making for water resource management, environmental protection, and public health safety.