地理空间分析
弹性(材料科学)
绿色基础设施
过程(计算)
持续性
计算机科学
城市规划
可扩展性
建筑
智慧城市
系统工程
工程类
环境规划
数据库
地理
土木工程
计算机安全
遥感
生态学
物理
考古
物联网
生物
热力学
操作系统
作者
Muniraju Hullurappa,Mohanarajesh Kommineni
出处
期刊:Advances in public policy and administration (APPA) book series
日期:2024-12-05
卷期号:: 373-396
标识
DOI:10.4018/979-8-3693-8069-7.ch017
摘要
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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