Paper detail

A multi-objective optimization framework for terrain modification based on a combined hydrological and earthwork cost-benefit

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 Høje 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.