AI-Enhanced Real-Time Turbidity Monitoring System Using Machine Learning
Nandhini A
Department of Computing Technologies SRM Institute of Science and Technology
Madhumitha K
Department of Computing Technologies SRM Institute of Science and Technology
Abstract—This project presents the design and implementation of an AI-Enhanced Real-Time Turbidity Monitoring System Using Machine Learning, developed to provide a reliable, low- cost, and intelligent solution for water quality assessment. Tur- bidity, which represents the degree of cloudiness in water caused by suspended particles, is a critical indicator of safety and ecological balance. High turbidity levels may indicate microbial contamination, treatment inefficiency, or environmental degra- dation, making accurate monitoring essential for applications ranging from municipal water supply to environmental protection and industrial processing. Traditional turbidity measurement methods are often expensive, laboratory-dependent, and limited in accuracy under dynamic field conditions, creating barriers to continuous and accessible monitoring. The proposed system overcomes these challenges by integrating a low-cost optical sensor with a microcontroller for real-time data acquisition, and a Random Forest machine learning algorithm for intelligent data interpretation. The microcontroller collects turbidity values from the sensor, which are then processed by the machine learning model to classify water quality more accurately than conventional single-sensor approaches. The Random Forest algorithm was chosen for its robustness to noise, ability to model non-linear relationships, and strong performance with multi-dimensional data. A Python-based application serves as the main program, enabling live predictions, result visualization through a user in- terface, and the possibility of extending the system to cloud-based storage for long-term monitoring and analysis. Experimental evaluation demonstrates that the system achieves significantly higher accuracy compared to standard techniques, confirming the role of machine learning in enhancing turbidity assessment. The modular design allows flexibility for future integration of additional parameters such as pH, temperature, and total dissolved solids (TDS). By combining affordable hardware with advanced data analytics, this project delivers a practical, scalable, and intelligent platform for continuous water quality monitoring. Ultimately, the system contributes to public health protection, sustainable water management, and environmental conservation, while also advancing the application of artificial intelligence in real-time environmental sensing.
Index Terms—Turbidity sensing, Random Forest, Arduino, real-time monitoring, environmental sensing, water quality, IoT.
