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Integrating Blue-Green Infrastructure Into Urban Development: A Data-Driven Approach Using AI-Enhanced ETL Systems
Abstract
This Chapter explores the integration of Blue-Green Infrastructure (BGI) into urban development using data-driven approaches enhanced by AI-powered ETL (Extract, Transform, Load) systems. As cities face increasing challenges due to climate change, sustainable urban planning practices such as BGI—which combines natural (green) and water management (blue) elements—are critical for resilience. However, the complexity of urban environments demands sophisticated data processing techniques to assess, design, and implement BGI solutions effectively. By adopting AI models within ETL processes, this paper presents a framework that automates the analysis of incoming environmental data, optimizes the planning process, and provides adaptive decision-making tools. The study highlights how AI-augmented ETL systems can process large volumes of geospatial, environmental, and infrastructure data, offering a more efficient, scalable, and intelligent approach to urban BGI integration. Case studies of smart city initiatives employing this technology are discussed, showcasing the benefits of data-driven BGI solutions in enhancing sustainability, urban resilience, and quality of life.
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