Enhancing sediment accumulation monitoring techniques in sewers will enable a better understanding of the build-up processes to develop improved cleaning strategies. Thermal sensors provide a solution to sediment depth estimation by passively monitoring temperature fluctuations in the wastewater and sediment beds, which allows evaluation of the heat-transfer processes in sewer pipes. This study analyses the influence of the flow conditions on heat-transfer processes at the water-sediment interface during dry weather flow conditions. For this purpose, an experimental campaign was performed by establishing different flow, temperature patterns, and sediment depth conditions in an annular flume, which ensured steady flow and room-temperature conditions. Numerical simulations were also performed to assess the impact of flow conditions on the relationships between sediment depth and harmonic parameters derived from wastewater and sediment-bed temperature patterns. Results show that heat transfer between water and sediment occurred instantaneously for velocities greater than 0.1 m/s, and that sediment depth estimations using temperature-based systems were barely sensitive to velocities between 0.1 and 0.4 m/s. A depth estimation accuracy of ±7 mm was achieved. This confirms the ability of using temperature sensors to monitor sediment build-up in sewers under dry weather conditions, without the need for flow monitoring.

Resilience assessment for urban drainage systems is a fundamental aspect of building resilient cities. Recently, some scholars have proposed the Global Resilience Analysis (GRA) method, which assesses resilience based on the functional performance of different system failure scenarios. Compared to traditional system dynamics methods, the GRA method considers the impact of internal structural failure on resilience but requires a large amount of computation. This research proposed an improved GRA method to enhance computational efficiency and practicality by reducing the number of system scenario simulations. Firstly, a hydrodynamic model of the drainage network of Haidian Island has been constructed using the Storm Water Management Model (SWMM) and Python. Secondly, the GRA method was improved using cluster analysis and convergence analysis to reduce the simulation scenarios. Thirdly, a resilience assessment index was established through system function functions, and two types of resilience enhancement measures, centralized and distributed, were proposed. The results show: (i) resilience assessment increases the computational efficiency by 25% compared to the traditional GRA method; (ii) the resilience index of the existing drainage network within Haidian Island is less than the design value (0.7) in all failure scenarios, indicating a lower level of recovery capability; (iii) compared to the centralized strategy, which is only effective when the system failure level is less than 9%, the distributed strategy enhances the resilience of the urban drainage system at a higher failure level (77%).

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.

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.

Real-time control (RTC) is a recognized technology to enhance the efficiency of urban drainage systems (UDS). Deep reinforcement learning (DRL) has recently provided a new solution for RTC. However, the practice of DRL-based RTC has been impeded by different sources of uncertainties. The present study aimed to evaluate the impact caused by the uncertainties on DRL-based RTC to promote its application. The impact of uncertainties in the measurement of water level signals was evaluated through large-scale simulation experiments and quantified using measures of statistical dispersion of control performance distribution and relative change of control performance compared to the baseline scenario with no uncertainty. Results show that the statistical dispersion of DRL-based RTC was reduced by 15.48%-81.93% concerning random and systematic uncertainties compared to the conventional rule-based control (RBC) strategy. The findings indicated that DRL-based RTC is robust and could be reliably applied to safety-critical real-world UDS.

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) 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 discloses the possibility of adopting SWMM_FLC as appropriate software to assist decision-makers to effectively minimize urban flooding volume at downstream urban drainage systems.

Recent studies suggested hybrid green-blue-gray infrastructures (HGBGI) as the most promising urban drainage systems that can simultaneously combine reliability, resilience, and acceptability of gray infrastructures (networks of pipes) with multi-functionality, sustainability, and adaptability of green-blue infrastructures (GBI). Combining GBI and gray measures for designing new urban drainage systems forms a nonlinear multimodal mixed integer-real optimization problem that is highly constrained and intractable. For this purpose, this study presents a simulation-optimization framework to optimize urban drainage systems considering HGBGI alternatives and different degrees of centralization. The proposed framework begins with the characterization of the site under design and drawing the base graph. Then, different layouts with different degrees of centralization are generated and hydraulically designed using a recent algorithm called hanging gardens algorithm. After introducing the feasible GBI to the model, we now perform second optimization to find the optimum distribution of GBIs in a way that minimizes total life cycle costs of GBIs and pipe networks. Finally, resiliency and sustainability of different scenarios are evaluated using several design storms that provide material for final assessment and decision-making. The performance of the proposed framework is evaluated using a real large-scale case study, a part of the city of Ahvaz in Iran. Finally, results are presented and discussed with recommendations for future studies.