Integrating Digital Twins and Artificial Intelligence Multi-Modal Transformers into Water Resource Management: Overview and Advanced Predictive Framework

Various Artificial Intelligence (AI) techniques in water resource management highlight the current methodologies’ strengths and limitations in forecasting, optimization, and control. We identify a gap in integrating these diverse approaches for enhanced water prediction and management. We critically analyze the existing literature on artificial neural networks (ANNs), deep learning (DL), long short-term memory (LSTM) networks, machine learning (ML) models such as supervised learning (SL) and unsupervised learning (UL), and random forest (RF). In response, we propose a novel framework that synergizes these techniques into a unified, multi-layered model and incorporates a digital twin and a multi-modal transformer approach. This integration aims to leverage the collective advantages of each method while overcoming individual constraints, significantly enhancing prediction accuracy and operational efficiency. This paper sets the foundation for an innovative digital twin-integrated solution, focusing on reviewing past works as a precursor to a detailed exposition of our proposed model in a subsequent publication. This advanced approach promises to redefine accuracy in water demand forecasting and contribute significantly to global sustainability and efficiency in water use.