Adaptive Ecological Urbanism: a Data-Driven Design Methodology for a Conceptual Prototype in Gokturk,Istanbul

Abstract

This study proposes adaptive, data-driven urban design methodology that integrates artificial intelligence with ecological performance to address contemporary environmental challenges. While smart city technologies enable advanced monitoring and optimization, they often prioritize operational efficiency over ecological quality. Conversely, ecological urban design approaches frequently remain static and insufficiently connected to real-time environmental data. This research argues that integrating these domains is essential for developing more responsive, resilient, environmentally informed urban environments. The originality of the study lies in the development of  unified methodological framework that positions artificial intelligence as an active design partner capable of interpreting environmental data, generating adaptive scenarios, and informing spatial decision-making processes. Through a comparative analysis of Copenhagen’s Cloudburst Management Plan, Singapore’s Digital Urban Twin, and Barcelona’s Sentilo platform, the research identifies transferable principles, including adaptive feedback loops, environmental monitoring systems, and measurable performance indicators. These principles are subsequently applied through a conceptual design prototype developed for the Göktürk district of Istanbul, utilizing satellite-derived environmental data and hypothetical IoT sensor networks to generate adaptive spatial strategies. The findings demonstrate how AI-supported design methodologies can enhance ecological performance, strengthen urban resilience, and contribute to the advancement of adaptive ecological urbanism as an emerging framework for sustainable urban design.