Digital Twin Based Smart Water Management System for Campus Consumption Analysis

Abstract

Water Distribution Networks (WDNs) require sustainable management, and monitoring is essential for saving water and increasing water use efficiency, while reducing water losses, known as Smart Water Management Systems (SWMS) and intelligent data analytics. This paper introduces the design and application of a campus-scale smart water management framework that integrates technologies like Internet of Things (IoT), digital twin, machine learning, etc., to facilitate live monitoring and decision support. Unlike simulation-based approaches, the proposed system has been deployed across the campus water distribution infrastructure, where sensor nodes continuously collect real-time water flow and consumption data. A digital twin is a virtual model of the physical network that is kept in synchronization and can be visualized in real time, which is compared to historical consumption patterns, and evaluation of hypothetical “what-if” scenarios, anomaly detection and operational planning. Machine learning techniques are used to assess consumption trends, spot unusual usage patterns, and assist with intelligent decision-making to manage water resources effectively.