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.