Multiperiod Optimization Framework for Urban Drainage System Planning: A Scenario-Based Approach

Designing urban drainage systems under deep uncertainty and for extreme climate conditions is a challenging task because various interrelated socioeconomic policies, urban characteristics, and hydraulics must be considered. To address such problems, scenario planning is the most well-known alternative solution for managing a wide range of future scenarios for any planning process under potential uncertainty. In the relevant literature, few efforts have been devoted to providing a long-term urban drainage system (UDS) planning solution to prevent several different levels of scenarios simultaneously. This study proposes a multiperiod optimization framework based on scenario planning. The proposed framework determines various system components and minimizes the regret costs (RCs) and total investment while satisfying a series of constraints: (1) scenario-optimal solutions are obtained from the multiperiod single scenario model; (2) common elements are identified across all scenarios; and (3) a compromise solution is determined to minimize the total regret cost considered in the multiperiod multiscenario model. The proposed framework outperformed a traditional design methodology in realizing long-term planning depending on the climate conditions considered in four different levels of scenarios. The RCs can provide flexible responses to future strategies under deep uncertainty and reduce the total investment in the UDS planning projection. In addition, aspects of the common elements process, identified in the initial period (i.e., Period 0) will be valid for a wide range of plausible future scenarios; therefore, identifying common elements is shown to be necessary for system planning under future scenarios. This study can be extended to different civic infrastructures with different standards and goals, thereby widening its applicability and providing additional insights for achieving a sustainable infrastructure service. This study has the potential to support the public and private sectors under uncertainty.