No abstract

Advances in sensing and controlling technology accelerate the application of real-time control (RTC) in stormwater management. RTC enables existing urban drainage systems (UDSs) to be retrofitted and autonomously controlled to adapt to increasing runoff volume and peak flow rates associated with extreme storm events. The goal of this research is to utilize RTC to promote resilient UDSs’ performance against the disturbances induced by climate and land cover changes. The present study investigates the performance and cost-effectiveness of RTC in enhancing flooding resilience. We developed an predictive RTC adaptation strategy at incremental control levels (0%, 33%, 67%, and 100%) using Python and the U.S. Environmental Protection Agency’s Storm Water Management Model (SWMM). A UDS located in Salt Lake City, Utah, USA, served as the case study. Results showed that the implementation of smart stormwater RTC enhances flooding resilience by up to 37% under future combined changes in rainfall intensity and land cover imperviousness. The partially controlled (67% and 33% control levels) UDS outperforms the fully controlled system (100% control level) when factoring in costs. RTC is a cost-effective adaptation strategy: one million USD investment enhances the resilience by up to 3.2% in a 30-year life cycle. These findings support establishing adaptation approaches to create a new generation of resilient and smart stormwater systems adaptive to uncertain rainfall and land cover conditions.

No abstract

The uncertainty of climate change and urbanization imposed additional stress for urban drainage systems (UDSs) by intensifying rainfall frequency and magnifying peak runoff rate. UDSs are among the stormwater infrastructures that can be controlled in real-time for mitigating downstream urban flooding. In this paper, a data-driven improved real-time control optimization-simulation tool called SWMM_FLC, which is based on the FLC (fuzzy logic control theory) and GA (genetic algorithm) was developed for smart decision-making of flooding mitigation. A calibrated and validated SWMM model was used for applying SWMM_FLC to explore the potential in reducing downstream flooding volume at UDSs. The results show that the data-driven enhanced GA optimization significantly reduces fuzzy system deviations from 0.22 (non_optmial scenario) to 0.07 (optimal scenario). The accumulated flooding volume reduction by up to 4.55% under eight artificial rainfall scenarios rules out the possibility of adopting SWMM_FLC as appropriate software to assist decision-makers to effectively minimize urban flooding volume at downstream urban drainage systems.