  The escalating risk of urban inundation has drawn increased attention to
urban stormwater management. This study proposes a multi-objective optimization
for terrain modification, combining the Non-dominated Sorting Genetic Algorithm
II (NSGA-II) with digital elevation model (DEM)-based hydrological cost factor
analysis. To reduce the precipitation erosive forces and runoff kinetic energy,
the resulting framework offers the possibility of efficiently searching
numerous solutions for trade-off sets that meet three conflicting objectives:
minimizing maximum flow velocity, maximizing runoff path length and minimizing
earthwork costs. Our application case study in H{\o}je Taastrup, Denmark,
demonstrates the ability of the optimization framework to iteratively generate
diversified modification scenarios, which form the reference for topography
planning. The three individual objective preferred solutions, a balanced
solution, and twenty solutions under regular ordering are selected and
visualized to determine the limits of the optimization and the
cost-effectiveness tendency. Integrating genetic algorithms with DEM-based
hydrological analysis demonstrates the potential to consider more complicated
hydrological benefit objectives with open-ended characteristics. It provides a
novel and efficient way to optimize topographic characteristics for improving
holistic stormwater management strategies.
