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Urban water-logging is a challenging environmental issue in most urban areas. Effective, process-oriented water-logging simulation and drainage optimization models have become imperative for urban storm water and emergency management. This research provides a solution to urban water-logging through integrating water-logging prediction and drainage optimization schemes based on theories of cellular automaton and multi-objective optimization. An urban water-logging model for uncertain flow is constructed by using cellular automaton and rules in consideration of urban surface fragmentation and space complexity instead of definite mathematical equations. For dynamic simulation, outputs of water depth and flooded areas are projected with inputs of rainfall, soil infiltration, plant interception, gully discharge, and outflow to its neighbors in each cell, at any moment. The drainage decision model for optimal solutions is designed to calculate the maximal amount of water to be pumped from flooded zones to candidate reservoirs with minimal energy cost by using a multi-objective optimization approach. This integrated approach was successfully applied in the DongHaoChong catchment (11 km2) watershed, a central urban area of Guangzhou, southern China, to forecast urban water-logging and optimize drainage decision-making. The results shows that the simulation model in this study is reliable as a whole and is capable of simulating uncertain flow at any position at any moment with minimal data input and parameters in an urban environment. An integrated solution to urban water-logging prediction and drainage optimization can optimize decision-making to alleviate urban water-logging.

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