IoT-Driven Water Treatment Systems for Real-Time Quality Assurance and Control Using Logistic Regression Model

Using Internet of Things (IoT) and machine learning to ensure safe and sustainable water supplies is a disruptive strategy. IoT-enabled architecture for next-generation water treatment systems using Raspberry Pi computing, high-performance machine learning methods, and logistic regression (LR). The proposed technology will revolutionize water treatment through real-time monitoring, quality assurance, and dynamic control. Networks of sensors use the Raspberry Pi platform as the central control unit to continually measure water quality. PH, turbidity, dissolved oxygen, chlorine content, and microbiological contamination are measured for water safety and purity. Water safety probability is predicted using an LR model trained on previous datasets using the processed data. The Raspberry Pi effortlessly integrates pumps, valves, and dosing systems for exact water quality modifications. The interface displays real-time water quality, model forecasts, and control status. Dashboards allow internet-enabled devices to monitor system performance, trends, and responses. Rapid alerts for significant events or quality threshold breaches are possible. Local and industrial water sources may benefit from LR-driven water treatment.