Deep Learning approaches for Water Distribution Networks: A Comparative Study on Pressure Estimation

Water Distribution Networks (WDNs) are dynamic systems that play a critical role in contemporary society by providing a consistent water supply for everyday needs. However, their efficient management faces significant obstacles due to growing urbanization, fluctuating water demands, limited resources, and substantial water losses, estimated at 32 billion cubic meters annually in developing countries. These challenges are exacerbated by insufficient financial resources and the constraints of conventional measurement and management technologies. To overcome these issues, Smart Water Distribution Networks (SWDNs) have been introduced as innovative solutions. By integrating Internet of Things (IoT) devices with artificial intelligence (AI) methodologies, these systems facilitate real-time monitoring, leakage detection, and pressure regulation through data-driven insights. Building on advancements in SWDNs, Digital Twins have emerged as transformative tools for WDNs. A Digital Twin connects the physical system to its digital counterpart through a continuous data flow. Through AI integration, they simulate network behavior, optimize operations, and derive actionable insights from sensor data, addressing key management challenges and improving efficiency. This work introduces a novel strategy to enhance SWDNs. Specifically, it outlines a deep learning approach for pressure prediction, designed for integration into a Digital Twin (DT) to enable predictive analytics, detect anomalies, and support real-time control. Using deep neural networks, the method achieves highly accurate hydraulic predictions with minimal input data, showcasing its potential to transform WDNs and promote their sustainability in addressing contemporary challenges.