Automated Detection of Contaminants in Wastewater Systems

Water is a vital natural resource that supports life, agriculture, industry, and ecosystems. However, rapid industrial growth, urban expansion, and population increase have significantly contributed to the contamination of water sources, particularly wastewater. If left untreated, wastewater can lead to severe environmental and health issues, highlighting the importance of effective contaminant detection. Traditional methods, such as laboratory testing and manual inspections, are often slow, expensive, and require expert intervention. To address these limitations, this project introduces an automated system that leverages machine learning techniques for the detection of contaminants in wastewater. The system integrates algorithms like Random Forest, Logistic Regression, Decision Tree, and K-Nearest Neighbors, selected for their efficiency in handling large datasets and classification accuracy. The solution is developed using Python, with Jupyter Notebook for data analysis and Flask for a responsive web interface. This platform allows users to input sensor-based water quality data—such as pH, temperature, turbidity, and the presence of hazardous substances—and receive immediate predictions on contamination levels. Designed to support wastewater treatment facilities, environmental bodies, and regulatory agencies, the system offers a scalable, accurate, and cost-effective alternative to traditional monitoring techniques. By enabling real-time detection and faster responses, it contributes to safeguarding public health and preserving environmental quality.