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 Terrain Modification Multi-Objective Optimization (TMMOO) framework, combining the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with digital elevation model (DEM)-based hydrological cost factor analysis. To reduce the precipitation's erosive forces and runoff's kinetic energy, TMMOO offers the possibility of efficiently searching numerous solution 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 TMMOO framework to iteratively generate diversified modification solutions, which form the reference for topography planning. Three DEM resolutions were inputted to validate the TMMOO framework's accuracy and applicability. Challenges remain in optimizing computational speed and seeking effective solutions at the finer resolution. Integrating genetic algorithms with DEM-based analysis demonstrates the potential to consider more complicated hydrological benefit objectives with open-ended characteristics. The result of this study provides a novel and efficient way to optimize topographic characteristics for improving holistic stormwater management strategies.