Optimizing Urban Water Networks through Integration of Quality Monitoring, Predictive Modeling and Leakage Management

Abstract

Urban water systems are under growing pressures from demand, climate change, and aging infrastructure. This paper presents a comprehensive water management framework for optimising urban water networks that combines on-line water quality monitoring, advance demand forecasting and continuous leakage control. The developed framework includes the usage of IoT-based sensors for obtaining real-time data, deployment of the SARIMA (Seasonal ARIMA) machine learning model for water demand forecasting, and machine learning algorithms to detect and control leaks. The proposed system has a good performance in terms of water consumption predictions with a high R2 value for commercial, domestic, industrial, and total water consumption sectors. Results say a great deal about the accuracy of the model to represent seasonal patterns and disturb (changing) water demand to offer actionable information for resource planning and to optimise systems. The framework provides a data-driven method for improving the efficiency, sustainability and resilience of the urban water network to ensure optimum water supply and minimize wastage.