IoT and Machine Learning-Based Sustainable Water Resource Management for Smart Cities

Abstract

 Rapid urban growth is putting a huge strain on freshwater availability worldwide. Traditional water management methods, which depend on periodic manual checks and reactive repairs, can no longer meet the needs of modern smart cities. This paper presents a framework that combines Internet of Things (IoT) sensor networks with machine learning algorithms to enable real-time, data-driven water resource management. The suggested system uses a variety of IoT sensors that monitor flow rate, water quality parameters (pH, turbidity, dissolved oxygen), pipeline pressure, and reservoir levels across a simulated urban water distribution network. Data from these sensors are sent through LoRaWAN and 5G NB-IoT protocols to a cloud platform. There, a hybrid machine learning pipeline combines Long Short-Term Memory (LSTM) networks, Random Forest classifiers, and Isolation Forest anomaly detectors to forecast demand, predict water quality, and locate leaks. Simulation experiments on a representative smart city dataset show that the proposed hybrid model achieves a leak detection accuracy of 95.8%, a demand forecasting RMSE of 4.23 liters per hour per household, and a potential water saving of up to 31.4% compared to traditional management methods. The system also provides real-time alerts for city officials through an interactive web dashboard. Findings show that integrating context-aware IoT and machine learning can greatly improve resource efficiency, lower non-revenue water losses, and support sustainable development goals (SDG 6) in rapidly growing urban areas.