IOT-ENABLED SMART WATER MANAGEMENT FOR RURAL SUPPLY SYSTEMS

Abstract : Water, covering approximately 70% of the Earth's surface, is a critical resource for sustaining life. Ensuring the safety and accessibility of potable water remains a global concern, as contaminated water poses significant health risks due to the presence of infectious agents and toxic substances. Traditional water quality assessment techniques are labor-intensive, time-consuming, and cost-inefficient, rendering them unsuitable for real-time applications. This project proposes a Water Quality Predictor (WQP) System leveraging machine learning techniques—specifically Support Vector Machine (SVM)—for real-time monitoring and classification of corporation tank water quality. Key water parameters such as pH, Dissolved Oxygen (DO), turbidity, and salinity are utilized to develop an accurate prediction model. The system incorporates cloud computing and artificial intelligence (AI) technologies to enhance prediction capabilities, aiming to prevent further degradation of water resources and support sustainable water management. The proposed WQP system enables continuous monitoring, effective classification, and timely decision-making for municipal water management.

Keyword Keywords: Water Quality Monitoring, Machine Learning, Support Vector Machine (SVM), Real-Time Monitoring, Cloud Computing, Artificial Intelligence, pH, Turbidity, Dissolved Oxygen.