Digital Transformation in the Water Distribution System based on the Digital Twins Concept

TL;DR

This paper presents a comprehensive Digital Twins platform for Water Distribution Systems that integrates IoT, AI, and optimization techniques to enhance monitoring, predictive maintenance, and sustainable management.

Contribution

It introduces the CAUCCES platform, combining advanced AI/ML models, optimization, and cybersecurity for improved water system management.

Findings

Effective water consumption prediction using AI/ML models.

Operational cost minimized through constraint programming.

Enhanced decision-making and system reliability.

Abstract

Digital Twins have emerged as a disruptive technology with great potential; they can enhance WDS by offering real-time monitoring, predictive maintenance, and optimization capabilities. This paper describes the development of a state-of-the-art DT platform for WDS, introducing advanced technologies such as the Internet of Things, Artificial Intelligence, and Machine Learning models. This paper provides insight into the architecture of the proposed platform-CAUCCES-that, informed by both historical and meteorological data, effectively deploys AI/ML models like LSTM networks, Prophet, LightGBM, and XGBoost in trying to predict water consumption patterns. Furthermore, we delve into how optimization in the maintenance of WDS can be achieved by formulating a Constraint Programming problem for scheduling, hence minimizing the operational cost efficiently with reduced environmental impacts. It also focuses on cybersecurity and protection to ensure the integrity and reliability of the DT platform. In this view, the system will contribute to improvements in decision-making capabilities, operational efficiency, and system reliability, with reassurance being drawn from the important role it can play toward sustainable management of water resources.