In facing growing challenges in cities and the environment, cities need to make informed decisions on where and how to allocate resources for infrastructure investments. Nature-based Solutions present a promising approach to urban environmental and socioeconomic challenges, but their successful integration into urban planning requires a nuanced understanding of both their benefits and limitations. This paper presents a preliminary Bayesian Network model designed to model the optimal integration of specific blue-green and gray Infrastructure solutions in hybrid systems for specific local contexts. The preliminary model considers a wide range of factors related to both infrastructure solutions, giving policymakers suitable arrangements tailored to their specific local conditions. With its probabilistic approach, the Bayesian Network model is a powerful tool for navigating the complex world of infrastructure planning. While the current model provides initial insights, its practical utility will be enhanced through the incorporation of higher-resolution data and application to specific case studies, enabling more accurate, context-sensitive recommendations. This research aims to connect data-driven modeling with practical urban planning, pushing forward the discussion on combining blue, green, and gray solutions for cities that are more sustainable and resilient.
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