On comparison of two-level and global optimization schemes for layout design of storage ponds

Optimization techniques have emerged as robust tools to aid the planning and design of urban drainage facilities in cost-effective ways. Such an effort was traditionally aided by heuristic methods (like genetic algorithm), which was generally time-consuming and also challenging in reaching convergence for large-scale problems with wide decision spaces. This study proposed a novel optimization method, denoted as two-level optimization (TO) scheme, for supporting rainwater storage pond design in an urban drainage system. Polynomial regression models were established as surrogate models to facilitate the solution of the optimization framework using traditional iteration algorithm. The TO scheme firstly sought the optimal layout of storage ponds on tributary sub-watersheds, and then proceeded to that of the mainstream one to yield the final solution. Through a case study, the TO scheme was compared with the traditional global optimization (GO) scheme where the physical simulation model was dynamically linked with genetic algorithm (GA) to seek the global optimal solution. The performance of two schemes under different constraint settings was analyzed. Effects of related issues such as start-point selection and mainstream design on tributary sub-watersheds were also discussed. The results showed that the proposed TO scheme is a prominent alternative to the traditional GO scheme to support urban water managers for a more science-based decision making towards storage pond implementation in large-scale practical problems.