Increased storm-water runoff and flooding and poor ecosystem health have brought increasing attention to catchment-wide implementation of green infrastructure (e.g., bioswales, rain gardens, permeable pavements, tree box filters, urban wetlands and forests, stream buffers, and green roofs) to replace or supplement conventional storm water management practices and create more sustainable urban water systems. Current green infrastructure (GI) practice aims at mitigating the negative effects of urbanization by restoring pre-development hydrology and ultimately addressing water quality issues at an urban catchment scale. However, the benefits of GI extend well beyond local storm water management, as urban green spaces are also major contributors to human health. Considerable research in the psychological sciences have shown significant human health benefits from appropriately designed green spaces, yet impacts on human wellbeing have not yet been formally considered in GI design frameworks. This work develops a novel computational green infrastructure (GI) design framework that integrates storm water management requirements with criteria for human wellbeing. A supervised machine-learning model is created to identify specific patterns in urban green spaces that promote human wellbeing; the model is linked to RHESSYS hydrological model to evaluate GI designs in terms of both water resource and human health benefits. An application of the framework to tree-based GI design in Dead Run Watershed, Baltimore, MD, shows that image-mining methods are able to capture key elements of human preferences that could improve GI design. The results also show that hydrologic benefits associated with tree-based features are substantial, indicating that increased urban tree coverage and a more integrated GI design approach can significantly increase both human and hydrologic benefits.
Read the article in Environmental Modelling and Software.