Designing Water Efficient Residential Landscapes with Agent-Based Modeling

Residential irrigation accounts for 40 to 70 percent of household water use in semi-arid and arid regions in the U.S. (Hilaire et al. 2008), which consumes valuable resources. However, landscaping can also produce benefits: trees can reduce the heat index around the home, which decreases air conditioning use and saves energy (Bernatzky 1982; Shashua-Bar, Pearlmutter, and Erell 2009). The focus of my research is an agent-based system for optimizing spatial arrangements of plants on a landscape to maximize their growth and minimize their water use. The optimization criteria include a natural phenomenon known as facilitation (Callaway 1995), which is observed in water-scarce environments when larger shrubs serve as benefactors to smaller annuals by generating conditions that protect them from harsh afternoon sun (Holzapfel et al. 2006). These shrubs, known as nurse plants, enable the annuals to survive on less water than they need in the full sun (Whiting, Roll, and Vickerman 2007). In my modeling and optimization system, called AgentScapes, each plant is an agent with growth requirements. A plant agent’s fitness at a given location is defined by a fitness function that includes those growth requirements and a penalty term designed to force facilitation. The landscape design is formulated as a combinatorial optimization problem with a discrete set of locations for each plant on a grid, a fixed number of plants, and a fitness function that defines the performance of a plant at a location. The objective is to select the best k locations from n possibilities, where k is the number of plants and n is the number of cells on the landscape. AgentScapes employs a new agent-based search algorithm designed to mimic how plant communities evolve over time in response to environmental conditions. In this algorithm, each agent acts independently to optimize its own position on the landscape through a series of local moves and random jumps.