A Novel Artificial Intelligence and Digital Twin Optimization Framework for Smart and Sustainable Neighborhood Assessment

Source abstract

Sustainable urban growth worldwide is increasingly challenged by rapid urbanization, environmental degradation, and inefficient resource management. Existing urban planning models often lack real-time monitoring, predictive analytics, and scenario-based decision support, which are essential for achieving urban sustainability. This study presents a framework that integrates artificial intelligence (AI), geographic information system (GIS) data, and digital twin simulations to identify spatial trends, forecast urban development trajectories, optimize resource allocation, and enhance urban planning processes. By integrating these technologies with the MOSTADAM Community Rating System, the framework was developed as a robust tool tailored to each area’s environmental and regulatory standards. Simulations of this framework over a 30-day period in a 5 km² urban area, resulted in a 20 percent reduction in energy use, a 25 percent decrease in water consumption, a 30.33 percent improvement in infrastructure resilience, and a 25 percent increase in green space. The results demonstrated the potential of AI-inspired urban planning to advance sustainable, adaptable, and intelligent urban development. Ultimately, the proposed framework supports smarter and more sustainable urban planning in Saudi Arabia and similar regions.