Urban drainage decision model for storm emergency management based on multi-objective optimization

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This study proposes a solution to prevent urban waterlogging, a challenging environmental issue, by integrating waterlogging prediction and drainage optimization schemes based on cellular automaton and multi-objective optimization theories. An urban waterlogging model for uncertain flow was constructed considering urban surface fragmentation and space complexity. For dynamic simulation, outputs of water depth and flooded areas were projected with inputs of rainfall, soil infiltration, plant interception, gully discharge, and outflow to its neighbors in each cell at any moment. Using a multi-objective optimization approach, the drainage decision model for optimal solutions calculated the maximal amount of water for pumping from flooded zones to candidate reservoirs with minimal energy cost. This integrated approach was successfully applied to the DongHaoChong catchment, an 11 km2 watershed in Guangzhou, southern China and validated by comparing the simulated flood areas with flooded points from two historical rainstorms (August 15, 2013 and June 23, 2014). The RMSE of the maximum waterlogging depth were 26.89 and 78.48 mm, respectively. Therefore, the proposed model was reliable and capable of simulating uncertain flow at any position and moment with minimal data input and parameters in an urban environment. Water-logged area and depth could be predicted based on the given rainfall assuming 1-, 10-, 50-, and 100-year storm data in the study area, and optimal drainage solutions could be obtained and verified. The proposed urban waterlogging prediction and drainage emergency models could optimize decision-making to improve emergency plans and reduce losses due to urban waterlogging.