Keywords
multi-objective optimization; decision support; green infrastructure; evolutionary algorithms
Start Date
26-6-2018 9:00 AM
End Date
26-6-2018 10:20 AM
Abstract
Sustainable management of water resources is challenged by numerous conflicting interests and objectives. Decision support tools (DSTs) are evolving to incorporate multiple objectives (e.g. economic, social, and environmental) into the development of water management plans, rather than focus only on minimizing cost. A recent version of the DST known as WMOST (Watershed Management Optimization Support Tool) uses a multi-objective evolutionary algorithm to generate management plan options that specify the location, quantity, and types of green infrastructure (GI) to implement in a watershed. In this study, we applied WMOST to a watershed in southern Massachusetts to develop management plan options that minimize cost, nutrient loads, and runoff. The resultant set of options are indicative of tradeoffs between these objectives, which were visualized multi-dimensionally to help inform the decision-making processes of stakeholders. Preliminary takeaways include: (1) implementing GI with small storage capacity on higher permeability soils leads to stronger performance in the objectives than implementing GI with large storage capacity on lower permeability soils and (2) implementing more units of GI on the categories of land use with low nutrient loads is more cost-effective than implementing fewer units of GI on the categories of land use with high nutrient loads.
Incorporating green infrastructure into water management plans using multi-objective optimization
Sustainable management of water resources is challenged by numerous conflicting interests and objectives. Decision support tools (DSTs) are evolving to incorporate multiple objectives (e.g. economic, social, and environmental) into the development of water management plans, rather than focus only on minimizing cost. A recent version of the DST known as WMOST (Watershed Management Optimization Support Tool) uses a multi-objective evolutionary algorithm to generate management plan options that specify the location, quantity, and types of green infrastructure (GI) to implement in a watershed. In this study, we applied WMOST to a watershed in southern Massachusetts to develop management plan options that minimize cost, nutrient loads, and runoff. The resultant set of options are indicative of tradeoffs between these objectives, which were visualized multi-dimensionally to help inform the decision-making processes of stakeholders. Preliminary takeaways include: (1) implementing GI with small storage capacity on higher permeability soils leads to stronger performance in the objectives than implementing GI with large storage capacity on lower permeability soils and (2) implementing more units of GI on the categories of land use with low nutrient loads is more cost-effective than implementing fewer units of GI on the categories of land use with high nutrient loads.
Stream and Session
Stream C: Integrated Social, Economic, Ecological, and Infrastructural Modeling
Session C2: Application of Decision Support Tools for Integrated Water Resources Management