Performance and implementation of low impact development - A review.

Understanding urban neighborhood differences in willingness to implement green infrastructure measures: a case study of Syracuse, NY

A comprehensive review of spatial allocation of LID-BMP-GI practices: Strategies and optimization tools.

Agent-based systems have been developed for many scientific applications and simulation studies to model a group of actors and their interactions based on behavioral rules. Agent-based models and multiagent systems simulate the emergence of system-level properties based on the actions of adaptive agents that interact with other agents, react to environmental signals, and optimize decisions to achieve individual goals. In water resources planning and management, agent-based modeling has been applied to explore, simulate, and predict the performance of infrastructure design and policy decisions as they are influenced by human decision making, behaviors, and adaptations. The goal of this paper is to provide a comprehensive introduction to agent-based modeling for water resources researchers, students, and practitioners, and to explore water resources systems as complex adaptive systems that can be studied using agent-based modeling. Agent-based modeling is defined, and the characteristics of complex ...

Using Agent-Based Modeling for Water Resources Planning and Management

‘There's a meadow outside my workplace’: A phenomenological exploration of aesthetics and green roofs in Chicago and Toronto

The traditional approaches to resolving urban stormwater problems include costly expansion of collections systems and/or creation of in-line storage and treatment capacity. An emerging ‘green’ infrastructure (GI) approach would instead reduce runoff sources. An agent-based model is used to explore the spatiotemporal emergence of rain gardens and green roofs in Point Breeze, a 175 ha neighbourhood in South Philadelphia, PA, under two different scenarios. In the first, household GI adoption rules consider only economic self-interest and the physical compatibility of each GI technology with lot characteristics. In the second scenario, the adoption rules are enhanced based on insights into the possible behaviour of property owners, as intuited by the designers/authors over a two-year period using a variety of empirical methods. In Scenario 2, relevant knowledge and perceptions are transferred to household decision-makers through social networks, and exposure to GI is assumed to diffuse GI innovation. The two ...

Gavin J. Riggall

Papers

Decentralised green infrastructure: the importance of stakeholder behaviour in determining spatial and temporal outcomes