Developing Blue-Green Infrastructure: Advantages and Challenges Through Natural Capital, Ecosystem Services, and Machine Learning

Addressing techniques on water resources towards sustainability and resilient cities relies on mechanisms that create conditions to foster the initial natural conditions and reduce the gap between human development and environmental necessity. Blue-green infrastructure (BGI) has emerged as a transformative solution for water adaptation, offering ecological, social, and economic benefits over gray infrastructure. Inspirated by natural processes, BGI not only restores environmental equilibrium but also enhances its ecosystem services, such as flood mitigation, water quality improvement, and urban cooling. Adopting blue-green infrastructure not only restores the natural conditions for water resources but also allows the recovery of the natural capital assets – the stock of natural resources – and its ecosystem services. These natural capital’s assets provide ecosystem services that benefit humans and their wellbeing, such as cleaning water, climate regulation, carbon sequestration, pollination, and water availability. However, the comprehension of geospatial indicators and conditions that influence the ecosystem services is a handful knowledge that leads to the implementation of blue-green technologies for water resources management. In a global warming context, characterized by more frequent and severe extreme events, such as floods and droughts, more adaptative and resilient infrastructure for water resources management is required, allowing an interconnected solution that encompass several parts of the society pursuing sustainability and benefit to human and environment. Meanwhile, the advancement in machine learning is also a promising mechanism that can be applied to water resources to handle prominent problems, offering improved decision support systems and often outperforming traditional models. Furthermore, the machine learning algorithms have been successfully used for integrated management of river-reservoir systems and real-time control of sewer systems. Hence, this research aims to develop a machine learning model to assess the impact of various spatial indicators on water ecosystem services. Initially, a random forest analysis is being undertaken to measure the correlation between several spatial indicators (or drivers) and ecosystem services. Some of the spatial indicators are land use, biome, precipitation, evapotranspiration, urbanization, etc. The ecosystem services evaluated in this study are based on the Nature’s Contributions to People (NCP) 6 (water quantity and flow regulation) and 9 (hazard regulation), from Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). This methodology will be applied to several continental basins worldwide, encompassing diverse conditions. Finally, this approach aims to quantify the influence of key drivers on water resources and guide decision-makers in adopting blue-green infrastructure. By doing so, it seeks to enhance ecosystem services, benefiting both society and the environment.