AI-Driven Real-Time Water Quality Monitoring System
Sakshi Kamble1 , Gayatri Mohite2, Rutuja Gunjal3 , Pratiksha Gholap4
1Computer Technology & Sou. Venutai Chavan Polytechnic, Pune
2Computer Technology & Sou. Venutai Chavan Polytechnic, Pune
3Computer Technology & Sou. Venutai Chavan Polytechnic, Pune
4Computer Technology & Sou. Venutai Chavan Polytechnic, Pune
Abstract - Real-time usage of water quality monitoring is vital for public health, agriculture, and environmental sustainability even if traditional laboratory-based techniques are still slow, expensive, and non-scalable. This work presents a complete end-to--end IoT system that continuously measures pH and water temperature by means of affordable sensors linked to a Raspberry Pi Zero. Temperature measurement using a K-type thermocouple and a MAX6675 module; precise voltage-to-pH conversion using an analog pH electrode with an ADS1115 16-bit ADC; Sent through the MQTT protocol, the raw sensor data is stored in a MySQL database on a central Mosquitto broker housed on a server machine. Through a unique multilingual graphical user interface built with CustomTkinter, one can access historical analysis, live visualization, and Water Quality Index (WQI) calculation.
The system also includes an entirely offline AI assistant powered by the local Ollama Llama 3.2 model that lets consumers inquire about the present water situation utilizing natural language. Expert, color-coded summaries suitable for regulatory agency submission are generated by an automated PDF report creator. With real-time alerts delivered in less than a second, extensive testing has shown that the temperature is correct to within 1.5°C and the pH to within 0.05. The suggested architecture is perfect for distant drinking water facilities, aquaculture farms, and industrial waste monitoring since it incorporates edge computing, safe MQTT communication, multilingual support (English, Hindi, Tamil), and local artificial intelligence inference. By providing a scalable, privacy-protecting solution to smart water management, this study helps to bridge cheap hardware with sophisticated analytics.
Key Words:- --Water quality monitoring, Internet of Things, Raspberry Pi Zero, MAX6675, ADS1115, MQTT, multilingual GUI, AI Assistant, Water Quality Index, real-time analytics.
