A Climate-Synchronized Digital Twin Paradigm for Multi-Infrastructure Urban Resource Orchestration Using Hybrid Intelligence

Abstract

Urban systems are increasingly becoming integrated systems where the disruptions propagate in energy, water, waste, and transportation systems, particularly during the climatic variability. The existing practices largely optimize each of these domains separately, which do not allow responding to dynamic and mutually supportive urban requirements. The article puts forth one digital twin conceptualization that keeps the multi-sector urban systems in sync in real-time through IoT streams and climate feeds to enable co-ordinated decisions. It is new in the sense that a hybrid intelligence layer, a hierarchical multi-agent learning, adaptive evolutionary optimization, and climate-informed predictive modelling are all deployed into one working environment. There is also integrated climate-equity coupled decision mechanism so that efficiency, resilience and equitable resource allocation can be achieved in heterogeneous urban zones. The framework also includes interdependency-sensitive modelling to model the cascading effects among subsystems. The evaluation results under normal and extreme conditions with simulation prove significant gains such as about 29% energy efficiency, 36% traffic delays, 32% emissions, and 34% fairness metrics increase, as well as quicker recovery when faced by climate-related disruptions. These findings show that the suggested solution will allow effective, dynamic, and fair management of the urban resources on a greater scale than the traditional single-domain remedies.