This project develops a novel computational Green Infrastructure (GI) design framework that integrates interactive, neighborhood-scale, collaborative design by multiple stakeholders ("crowd-sourced" design) with multi-scale models of ecosystem and human impacts. The following research questions are being addressed: (1) How well does coupling of site-scale ecohydrology with catchment-scale hydraulic routing improve predictions? (2) How well can stakeholder preferences be predicted using design image feature extraction and machine learning? (3) What interactive optimization and visualization techniques lead to the most rapid and complete consensus among diverse stakeholders? (4) Do stakeholders using interactive cyberinfrastructure tools consider more options and explore more of the GI design space?
A "crowd-sourced" design framework is developed to enable stakeholders to interactively create and evaluate potential GI designs that reflect consideration of the full breadth of social, economic, and environmental criteria. The research questions are evaluated in diverse neighborhoods within three urban catchments in the Baltimore Ecosystem Study, working closely with environmental non-governmental organizations to ensure that the results will provide significant benefits to community stakeholders. The models developed in this project are the first to integrate criteria for human and ecosystem wellbeing with site- and watershed-scale hydrologic processes, a key advance for improving understanding and implementation of GI design. Map and image visualization identifies which visualization approaches best support improving stakeholder engagement for achieving consensus using interactive collaborative design. This makes technical advances in interactive optimization and model parameterization accessible to the broad range of stakeholders, from regulators and planners to contractors and homeowners.
