A Novel Methodology to Support Optimal Sustainable Drainage Systems Allocation for Large Cities: Coupling Hydrological Modeling with Mixed-Linear Optimization

Sustainable Urban Drainage Systems (SUDS) are environmentally-friendly practices that have been increasingly implemented to mitigate the hydrologic effects of an accelerated growth of urban centers. However, many cities are having difficulties in planning SUDS implementations and understanding their performance and efficiency. Decision making regarding SUDS sitting is a complex process, as it involves the evaluation of a large number of environmental, technical and financial considerations. Unfortunately, the scarcity of detailed information has precluded the implementation of existing stormwater models that simulate the effect of SUDS in developing and fast-growing cities. Therefore, a methodology that can support SUDS decision making using generally public access information is essential to close this gap in knowledge. This study proposes a novel methodology that couples a hydrologic urban drainage model and Mixed Integer Linear Programming (MILP) to determine where is most beneficial to locate SUDS. The methodology uses generally available information to estimate the effect of different SUDS configurations in both runoff quantity and quality (in terms of total suspended solids). 3-meters-resolution satellite images are used to develop a land use classification that allows a rough estimate of total suspended solids. The methodology was applied to the urban sub-catchments of Bogotá (Colombia), comprising an area of 350 km2. Preliminary results showed that the prioritized sub-catchments were highly dependent on land availability and the optimal solution did not necessarily involve the selection of the “best” isolated sub-catchments. Finally, the methodology selected the best sites for SUDS sitting from a set of feasible potential sites previously identified in the public space. This methodology constitutes the first optimization tool of its kind for large cities and will be especially useful under information scarcity context.