Integrating Social Equity into Multi-Objective Optimization for Urban Low Impact Development
Robert Seth Herbst1 ,Teresa Culver1,Lawrence Band1,2
1.

Department of Engineering Systems and Environment, University of Virginia, Charlottesville, Virginia
2.

Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia

Results

Introduction• Increasing access to stormwater low impact development (LID) and other green infrastructure in underserved areas is a prominent environmental
justice issue and becoming an important priority in the planning of future locations for urban LIDs.1,2,3• Race is a major predictor of environmental inequity in the US, as black and other minority groups have historically been intentionally disenfranchisedfrom neighborhood investment, the infrastructure planning processes, and other arenas of public decision making.4• This has led to great disparities in the distribution of LIDs and other green infrastructure within cities.
• LIDs can provide a range of other benefits outside the scope of hydrologic and ecologic improvements that are pertinent to public health. LIDs cancontribute to the beautification of urban environments often associated with improved mental health.5,6• Results include near-optimal area allocations for all LIDs.
• The below 4 maps show near optimal total LID area allocations to eachsubbasin for all four objectives- 25 million dollar limit build cost scenario.

Methods• A genetic algorithm was developed and used to identify near-optimal locations, types, and sizes of stormwater LIDs to minimize peak flow and total
runoff production, while maximizing implementation in more marginalized communities.
• The specially formulated genetic algorithm was linked to the stormwater management model (SWMM) application programming interfaces(PySWMM7 and SWMMIO8) to discover near optimal locations, types, and sizes of LIDs for the Meadow Creek Watershed.
• Presented LID optimization results are based on a single design storm (SCS type II 24-hr 1 inch) simulated in SWMM.

Creating a social equity index for the Meadow Creek
Subbasins

Coupling a Genetic Algorithm and SWMM

• Geospatial demographic census, landcover, and urban environmental
risk data was integrated into a “social equity index” by which eachMeadow Creek subbasin was weighted (prioritized) in the algorithm for
LID allocation/implementation in SWMM.
• Higher index scores indicate higher levels of marginalization,
environmental risk, and need for green infrastructure.

• PYSWMM and SWMMIO allowed for LID scenarios to be rapidly
generated and coupled to a genetic algorithm .
SWMM Model Workflow

Genetic Algorithm• Pareto optimal fronts allow for a cost benefit analysis and anassessment of tradeoffs across a budget range.

• Denominator is a min function for last three social equity factors

Maximizing implementation in marginalized communities and meeting multiple objectivesConclusions• Maximize total LID implementation in marginalized communities (written as a minimization
summation for uniformity among objective functions):•`# 𝒐𝒇 𝑰𝒏𝒅𝒊𝒄𝒂𝒕𝒐𝒓𝒔 −𝑺𝑬𝑰𝒊𝒏Social Context Score = σ𝒊=𝟏
(𝑨𝒍𝒍𝒐𝒄𝒂𝒕𝒆𝒅 𝑳𝑰𝑫 𝑨𝒓𝒆𝒂Τ𝑭𝒆𝒂𝒔𝒊𝒃𝒍𝒆 𝑳𝑰𝑫 𝑨𝒓𝒆𝒂)

••

(1)
𝒊

•

where SEIi denotes the social equity index weight for subbasin i.
• Achieve a balance between minimizing peak flow, minimizing total runoff, and maximizing stormwater LID allocation in marginalized communities.
Relative Composite Score =𝑨+𝑺𝒄𝒆𝒏𝒂𝒓𝒊𝒐 𝑷𝒆𝒂𝒌 𝑭𝒍𝒐𝒘
𝑩+ 𝑺𝒆𝒏𝒂𝒓𝒊𝒐 𝑻𝒐𝒕𝒂𝒍 𝑹𝒖𝒏𝒐𝒇𝒇
𝑪+𝑺𝒄𝒆𝒏𝒂𝒓𝒊𝒐 𝑺𝒐𝒄𝒊𝒂𝒍 𝑪𝒐𝒏𝒕𝒆𝒙𝒕 𝑺𝒄𝒐𝒓𝒆++𝑨+𝑴𝒊𝒏 𝑺𝒄𝒆𝒏𝒂𝒓𝒊𝒐 𝑷𝒆𝒂𝒌 𝑭𝒍𝒐𝒘
𝑩+𝑴𝒊𝒏 𝑺𝒄𝒆𝒂𝒓𝒏𝒊𝒐 𝑹𝒖𝒏𝒐𝒇𝒇
𝑪+𝑴𝒊𝒏 𝑺𝒄𝒆𝒏𝒂𝒓𝒊𝒐 𝑺𝒐𝒄𝒊𝒂𝒍 𝑪𝒐𝒏𝒕𝒆𝒙𝒕 𝑺𝒄𝒐𝒓𝒆(2)• A, B, and C are weights that determine the relative importance of each objective in the relative composite score.
• In this study, weights were chosen such that each term being summed contributes evenly to the relative composite score.
• The genetic algorithm is designed to discover LID locations, types, and sizes that minimize equation 1 and 2 along with peak flow and total runoff.

Optimal LID allocations depends on the objective
A social equity index can be integrated into a equation 1 or a similar
equation to help optimize equitable LID allocations.
A multi objective function such as equation 2 can be used to discover
optimal LID allocations that meet local hydrological and social goals.

References
1. Garcia-Cuerva, L., Berglund, E. Z., & Rivers, L. (2018). An integrated approach to place Green Infrastructure strategies in marginalized communities and evaluate
stormwater mitigation. Journal of Hydrology, 559, 648–660. https://doi.org/10.1016/j.jhydrol.2018.02.066
2. Heck, S. (2021). Greening the color line: historicizing water infrastructure redevelopment and environmental justice in the St. Louis metropolitan region. Journal ofEnvironmental Policy and Planning. https://doi.org/10.1080/1523908X.2021.1888702
3. Heckert, M., & Rosan, C. D. (2016). Urban Forestry & Urban Greening Developing a green infrastructure equity index to promote equity planning ଝ. Urban
Forestry & Urban Greening, 19, 263–270. https://doi.org/10.1016/j.ufug.2015.12.011
4. Wright, W. J. (2021). As Above, So Below: Anti-Black Violence as Environmental Racism. Antipode, 53(3), 791–809. https://doi.org/10.1111/anti.12425
5. Kondo, M. C., Low, S. C., Henning, J., & Branas, C. C. (2015). The Impact of Green Stormwater Infrastructure Installation on Surrounding Health and Safety.
105(3), 114–121. https://doi.org/10.2105/AJPH.2014.302314
6. Roe, J. J., Ward Thompson, C., Aspinall, P. A., Brewer, M. J., Duff, E. I., Miller, D., Mitchell, R., & Clow, A. (2013). Green space and stress: Evidence from cortisol
measures in deprived urban
communities. International Journal of Environmental Research and Public Health, 10(9), 4086–4103.
https://doi.org/10.3390/ijerph10094086
7. McDonnell, Bryant E., Ratliff, Katherine M., Tryby, Michael E., Wu, Jennifer Jia Xin, & Mullapudi, Abhiram. (2020, April 14). PySWMM: The Python Interface toStormwater Management Model (SWMM). Zenodo. http://doi.org/10.5281/zenodo.3751574
8. Erispaha, Adam (2021). SWMMIO version 0.4.8. MIT LICENSE.https://pypi.org/project/swmmio/