Abstract Submission: This study develops a decision-support tool based on a Bayesian network model to evaluate and optimize Nature-Based Solutions (NbS) for enhancing urban resilience. Focusing on Istanbul, a city vulnerable to climate risks such as extreme heat, flooding, and air pollution, the study enhances an existing theoretical model by integrating high-resolution meteorological data and an urban heat island map. The proposed method improves the model’s accuracy and resolution, allowing for more precise identification of NbS that effectively mitigate key climate risks—temperature reduction, stormwater management, humidity control, and air quality improvement. A key innovation is the incorporation of district-level data, ensuring that the proposed NbS are tailored specifically to Istanbul’s unique environmental and urban conditions. The study also extends the model's utility by developing a framework for adapting it to other global cities with diverse climate challenges and land-use patterns. This hybrid methodology provides both city-specific insights and a transferable tool for urban resilience planning worldwide. The findings will contribute to sustainable urban development and inform policy recommendations for incorporating NbS into urban planning and climate adaptation strategies.
