A framework for real-time operation of urban detention reservoirs: Application of the cellular automata and rainfall nowcasting.

Detention reservoirs are employed in urban drainage systems to reduce peak flows downstream of reservoirs. In addition to the volume of detention reservoirs, their operational policies could significantly affect their performance. This paper presents a framework for the real-time coordinated operation of detention reservoirs using deep-learning-based rainfall nowcasting data. Considering the short concentration time of urban basins, the real-time operating policies of urban detention reservoirs should be developed quickly. In the proposed framework, a cellular automata (CA)-based optimization algorithm is linked with the storm water management model (SWMM) to optimize real-time operating policies of gates at the inlets and outlets of detention reservoirs. As CA-based optimization models are not population-based, their computational costs are much less than population-based metaheuristic optimization techniques such as genetic algorithms. To evaluate the applicability and efficiency of the framework, it is applied to the east drainage catchment (EDC) of Tehran metropolitan area in Iran. The results illustrate that the proposed framework could reduce the overflow volume by up to 60%. For complete flood control in the study area, in addition to the real-time operation of detention reservoirs, constructing five tunnels with a total length of 13200 m is recommended. To evaluate the performance of the CA-based optimization model, its results are compared with those obtained from the non-dominated sorting genetic algorithm III (NSGA-III). It is shown that the CA-based model provides similar results with only 5% of the run-time of NSGA-III. A sensitivity analysis is also performed to evaluate the effects of optimization models' parameters on their performance.

A resilience-based robustness evaluation framework for sustainable urban flood management under uncertainty.

Urban drainage systems (UDSs) may experience failure encountering uncertain future conditions. These uncertainties arise from internal and external threats such as sedimentation, blockage, and climate change. In this paper, a new resilience-based framework is proposed to assess the robustness of urban flood management strategies under some distinct future scenarios. The robustness values of flood management strategies are evaluated by considering reliability, resiliency, and socio-ecological resilience criteria. The socio-ecologic resilience criteria are proposed considering the seven principles of building resilience proposed by Biggs et al. (2012). The evidential reasoning (ER) approach and the regret theory are utilized to calculate the total robustness of the flood management strategies. In this framework, the non-dominated sorting genetic algorithms III (NSGA-III) optimization model and the storm water management model (SWMM) simulation model are linked and run to quantify the criteria. The novelty of this paper lies in presenting a new framework to increase the sustainability and resilience of cities against floods considering the deep uncertainties in the main economic, social, and hydrological factors. This methodology provides policies for redesigning and sustainable operation of urban infrastructures to deal with floods. To evaluate the applicability and efficiency of the framework, it is applied to the East drainage catchment of the Tehran metropolitan area in Iran. The results show that real-time operation of existing flood detention reservoirs, along with implementing five new relief tunnels with a construction cost of 37.1 million dollars, is the most robust non-dominated strategy for flood management in the study area. Comparing the results of the proposed framework with those of a traditional framework shows that it can increase the robustness value by about 40% with the same implementation cost.