A Bayesian Network model to integrate blue-green and gray infrastructure systems for different urban conditions.

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.