A conceptual architecture for AI-assisted Digital Twins in natural resource management

The management of natural resources is increasingly critical and challenging due to complex interactions among environmental, industrial, and societal processes. Traditional approaches often fail to integrate heterogeneous data, limiting predictive and decision-support capabilities. This study presents a conceptual architecture for an Artificial Intelligence (AI)-assisted Digital Twin (DT) of the Centre-Val de Loire region, designed to unify time-dependent multi-source data. Based on the ENVRI Reference Model, it covers Science, Information, Computational, Engineering, and Technology layers, defining standardized data exchange, communication protocols, and prototype functionalities. A proof of concept FIWARE implementation supports ingestion, monitoring and analytical services for piezometric and meteorological data, exemplified through groundwater dynamics in the Beauce aquifer. It integrates daily observations from 53 piezometric stations over more than five years, managing approximately 2.8 million records in a containerized environment. Results show that the proposed DT architecture can enhance sustainability-oriented decision making, integrating heterogeneous data and predictive analyses while enabling collaboration across scientific and technical domains. Its modular design offers a replicable template for future AI-assisted environmental DTs, scalable to larger regions. Hence, this work illustrates how DTs can improve environmental monitoring and understanding, providing a pathway toward resilient, data-driven management of natural resources. • AI-assisted Digital Twin for natural resource management. • Data architecture design based on the ENVRI Reference Model. • Integration of heterogeneous, temporally correlated environmental data. • Use cases, functions, data exchange and communication protocols. • Viewpoints UML-modeling and complete operative PoC.