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This study aims to introduce a generic solution in the context of a multicriteria decision making (MCDM) platform to (1) facilitate the optimization of hybrid (de)centralized urban drainage infrastructures with many decisions and often conflicting objectives (reliability, resilience, sustainability, and construction costs); (2) investigate the trade-offs between performance indicators and system configuration; and(3) avoid conflicts between optimization analysts and decision makers by involving the latter in different stages of planning. For this purpose, first, all optimum design scenarios of hybrid urban drainage systems (UDSs) are generated through multiobjective optimization (MOO). Then a platform based on MCDM is presented to comprehensively analyze the solutions found by MOO and to rank the solutions. For the sake of demonstration, the proposed framework is applied to a real case study. The results confirm the ability of the proposed framework in handling many decisions, objectives, and indicators for solving a complex optimization problem in a reasonable time by delivering realistic solutions. In addition, the results demonstrate the important role of the degree of (de)centralization (DC) and the layout configuration in obtaining optimal solutions.

Operational and structural interventions in the field of stormwater management are usually planned based on long-term simulations using rainfall-runoff models. The simulation results are often highly uncertain due to imperfections of the model structure and inevitable uncertainties of input data. The trend towards monitoring of combined sewer overflows (CSO) structures produces more and more data which can be used to replace parts of the models and reduce uncertainty. In this study we use highly resolved online flow and quality monitoring data to optimize static outflow settings of CSO tanks. In a second step, the additional benefit of real time control (RTC) strategies is assessed. In both cases the aim is the reduction of CSO emissions. The methodology is developed on a conceptual drainage system with two CSO tanks and then applied to a case study area in Southern Germany with six tanks. A measured time series of six months is sufficient for reliable optimization results in the conceptual catchment as well as in the case study area system. In the investigated system the choice of the optimization objective (minimum overflow volume or total suspended solids (TSS) load) had no significant influence on the result. The presented method is particularly suitable for areas in which reliable monitoring data are available, but hydrological parameters of the catchment areas are uncertain. One strength of the proposed approach lies in the accurate representation of the distribution of emissions between the individual CSO structures over an entire system. This way emissions can be fitted to the sensitivity of the receiving water body at the specific outlets.

Structural resilience describes urban drainage systems’ (UDSs) ability to minimize the frequency and magnitude of failure due to common structural issues such as pipe clogging and cracking or pump failure. Structural resilience is often neglected in the design of UDSs. The current literature supports structural decentralization as a way to introduce structural resilience into UDSs. Although there are promising methods in the literature for generating and optimizing decentralized separate stormwater collection systems, incorporating hydraulic simulations in unsteady flow, these approaches sometimes require high computational effort, especially for flat areas. This may hamper their integration into ordinary commercially designed UDS software due to their predominantly scientific purposes. As a response, this paper introduces simplified cost and structural resilience indices that can be used as heuristic parameters for optimizing the UDS layout. These indices only use graph connectivity information, which is computationally much less expensive than hydraulic simulation. The use of simplified objective functions significantly simplifies the feasible search space and reduces blind searches by optimization. To demonstrate the application and advantages of the proposed model, a real case study in the southwest city of Ahvaz, Iran was explored. The proposed framework was proven to be promising for reducing the computational effort and for delivering realistic cost-wise and resilient UDSs.