Stochastic Optimization Model for Supporting Urban Drainage Design under Complexity

Abstract

A stochastic optimization model for urban drainage design (SODD) was proposed in this study to help analyze the trade-off between investment and acceptable flood damage in urban drainage designs considering effects of both uncertainty and climate change. The simulation model [i.e., Storm Water Management Model (SWMM)], driven by designed rainfall either from existing intensity-duration-frequency (IDF) curves or future ones subjected to climate-change conditions, was used to simulate flooding scenarios. The generalized uncertainty analysis estimation (GLUE) and Monte Carlo simulation methods were employed to quantify the system reliability, which was adopted in the constraints of the optimization model. The results from a case study showed that the deterministic optimization was computationally efficient, with no randomness encountered in hydrological simulation, although its solution was hardly reliable in achieving the target for flood mitigation. The stochastic version, on the other hand, could offer richer information on system reliability and help managers make a more robust decision. The results also revealed that the rainfall extremes under the impact of climate change could significantly affect system investment. The proposed method is advantageous in facilitating cost-effective planning toward a risk-based drainage design in light of various complexities.