Smart Cities and Blue-Green Infrastructure

Abstract

Smart cities are increasingly adopting sustainable practices to improve urban living while addressing environmental concerns. One key component of this movement is blue-green infrastructure (BGI), which integrates water management (blue) and vegetation (green) systems to enhance urban resilience, biodiversity, and sustainability. In tandem with these efforts, machine learning (ML) plays a critical role in optimizing the design, implementation, and maintenance of BGI in smart cities. ML algorithms provide the ability to process large datasets from sensors, weather forecasts, and environmental variables to create predictive models that enhance decision-making in urban planning. This paper explores the convergence of smart city initiatives, blue-green infrastructure, and machine learning, examining how advanced data-driven technologies can support sustainable urban development. Key applications include real-time monitoring of water management systems, predicting environmental impacts, optimizing resource allocation, and automating the management of green spaces. The integration of machine learning with blue-green infrastructure offers cities a transformative pathway toward more sustainable, resilient, and efficient environments.