Urban Water Quality Prediction Leveraging Real-Time Data Analytics

This study investigates the use of widely available data sources and machine learning algorithms to forecast urban water quality. The model uses algorithms to assess data from sensors that measure the pH, turbidity, and pollutant levels in water bodies, including Random Forest, Support Vector Machines (SVM), and XGBoost. The system can be used for real-time water quality monitoring in urban settings on inexpensive hardware, such as Raspberry Pi and Arduino Nano. The suggested method shows excellent accuracy in forecasting the quality of the water, offering insightful information for municipal water management and guaranteeing public health and safety. The model's ability to accurately forecast trends in water quality can support proactive management approaches, as demonstrated by the results.