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Sustainable urban stormwater management presents challenges due to uncertainties and conflicting stakeholder preferences. In this regard, our research introduces a comprehensive framework that integrates self-organization map (SOM) and many-objective robust decision-making (MORDM) methods to bridge the knowledge gap between deep uncertainty and conflict resolution. To do so, first, MORDM generates optimal strategies for the implementation of low-impact development (LID) practices and quantifies their robustness under different uncertain scenarios. Then, the results are integrated into the SOM framework, wherein multi-dimensional decision space can be classified and stakeholders are enabled to navigate their satisfaction to reach a subset of robust strategies. Finally, to determine the most suitable management strategy, a novel game theory-based group multicriteria decision-making (GT-GMCDM) method is employed, which can effectively consider stakeholders' conflicts in the selection process. Results from applying the proposed methodology in District 10 of Tehran municipality, Iran, demonstrate that the selected alternative can reduce TSS contamination load and runoff volume (RV) by 30% and 27%, respectively. Indeed, findings prove that in addition to improving stormwater quality and quantity, the suggested methodology can simultaneously consider stakeholders' preferences and find a reliable strategy. This study also offers valuable insights into robust stormwater management, with a focus on ensuring the sustainability of urban environments. [ABSTRACT FROM AUTHOR]

The main purpose of this research is to investigate the effect of increasing porosity on concrete porous pavement (PP) clogging control. The delay due to the clogging process has been observed in order to extend pavement service life and improve storm water management performance in long term. By adding pumice to the pervious concrete mix design, porosity and permeability increased; hence, additional porosity for the accumulation of suspended solids has been formed. The results showed that adding the pumice as part of the concrete aggregates produces positive effect on the initial permeability. Affected by clogging, permeability of the specimens in which pumice was added reduced only 26%, 10%, and 29%, showing that the procedure successfully delays clogging and extends service life of the PP. Although adding pumice reduces the compressive strength, it is still within the acceptable range. By comparison the observed data with the results of the permeability prediction models, it is demonstrated that the models properly predict permeability of the pavement over time, in more than 73% of the observed data and the results of the prediction models merely less than 10% difference observed. The rainfall-runoff simulation indicates that adding pumice as part of the permeable concrete aggregates results in delaying of the clogging and improving the performance of concrete increasing the service life of permeable pavements. [ABSTRACT FROM AUTHOR]

• Simple Urban Flood Resilience Index (SUFRI) is employed for green infrastructure planning. • Urban flood management strategies are identified through the lens of resiliency. • Utilitarian-based Social Welfare is utilized for evaluating the social behavior of strategies. • Sustainable flood management plan is proposed with high resiliency and social acceptability. Urban policymakers have long searched for stormwater management plans that incentivize stakeholders to adopt Green Infrastructure (GI) while effectively reducing the vulnerability of drainage systems. In this regard, our research introduces a novel framework to develop GI strategies that provide both hydrological resiliency and social acceptance. To achieve this, first, using a coupled Stormwater Management Model (SWMM) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II), optimal alternatives for GI planning were generated. In the optimization process, we used a novel Simple Urban Flood Resilience Index (SUFRI) to consider the internal performance of the system in identifying resilient plans. Derived management strategies warrant runoff volume reduction and resilience improvement up to 31.3% and 55.1%, respectively. In the next step, Utilitarian-based Social Welfare (USW) was employed to clarify the socio-economic behavior of management strategies. Results indicate that while financial incentives significantly motivate developers to implement GI, they cannot guarantee high social welfare, and achieving a sustainable solution requires evaluating both SUFRI and USW layers under different subsidy levels. Visualizing the SUFRI layer revealed a critical failure in the resiliency trend of solutions that cannot be detected by evaluating simpler metrics, such as runoff volume reduction. This highlights the importance of the SUFRI method in conducting deeper evaluations and preventing financial waste. Finally, we navigated the intersection of USW and SUFRI measures to reach an ideal management plan with optimal supporting level. Our findings showed that the selected solution with the highest social acceptability can improve the resiliency of the system by 29 %. This study is a novel combination of the hydrological and social aspects of stormwater management, enabling decision-makers to take significant steps towards sustainable urban development. [ABSTRACT FROM AUTHOR]

Stormwater runoff is a major concern in urban areas which is mostly the result of vast urbanization. To reduce urban stormwater runoff and improve water quality, low impact development (LID) is used in urban areas. Therefore, it is vital to find the optimal combination of LID controls to achieve maximum reduction in both stormwater runoff and pollutants with optimal cost. In this study, a simulation–optimization model was developed by linking the EPA Storm Water Management Model (SWMM) to the Multi-Objective Particle Swarm Optimization (MOPSO) using MATLAB. The coupled model could carry out multi-objective optimization (MOO) and find potential solutions to the optimization objectives using the SWMM simulation model outputs. The SWMM model was developed using data from the BUNUS catchment in Kuala Lumpur, Malaysia. The total suspended solids (TSS) and total nitrogen (TN) were selected as pollutants to be used in the simulation model. Vegetated swale and rain garden were selected as LID controls for the study area. The LID controls were assigned to the model using the catchment characteristics. The target objectives were to minimize peak stormwater runoff, TSS, and TN with the minimum number of LID controls applications. The LID combination scenarios were also tested in SWMM to identify the best LID types and combination to achieve maximum reduction in both peak runoff and pollutants. This study found that the peak runoff, TSS, and TN were reduced by 13%, 38%, and 24%, respectively. The optimal number of LID controls that could be used at the BUNUS catchment area was also found to be 25. [ABSTRACT FROM AUTHOR]

Urbanization growth leads to shifting hydrological factors which in turn represent a challenge for urban stormwater management. In this situation, policy strategies can be employed by governments to incentivize stakeholders to use green infrastructure for effective runoff management. This study presents a novel framework evaluating the application of Low-Impact Development (LID) practices for runoff quality and quantity management from a socio-economic point of view. The proposed framework investigates the potential of cooperative game theory to find a fair allocation based on the concept of justice. In this regard, first, the Non-dominated Sorting Genetic Algorithm (NSGA-II) was coupled with the Storm Water Management Model (SWMM) to find optimal runoff management scenarios considering minimization of runoff volume, peak flow, Total Suspended Solid (TSS) load, and costs of LIDs implementation. In the next step, in order to conflict resolution, stakeholders' preferences and interactions were incorporated into the cooperative game theory to determine management scenarios' worth of coalition. In the third step, benefits are allocated among stakeholders by defining three different sharing rules. Then, to select the most capable runoff management scenario, social choice methods of Condorcet choice, Borda scoring, and median voting rule support cooperative game decisions. The proposed methodology is explored in District 10 of Tehran municipality in Iran. According to the findings, solutions with stricter plans for runoff management correspond to more worth of coalition. In addition, the higher worth of coalition, the higher the reduction of peak flow, TSS load, and the total runoff volume. The results also reveal that the Borda scoring procedure tends to select scenarios with less reduction in TSS load, runoff volume, and peak flow in comparison with the others, and as expected, the minimum value for the worth of coalition. Additionally, the cooperative game targets a management scenario with the highest worth of coalition and the maximum value for the reduction in runoff volume, peak flow, and TSS removal. Our framework warrants runoff volume, TSS load, and peak flow can be reduced by 32.8, 31.9, and 32.7 percent, respectively. This framework, in addition to incentivize the stakeholders for implementing runoff management strategies, can effectively consider their conflict of interests in the decision-making process. [Display omitted] • Urban runoff quality and quantity are managed using cooperative game theory. • The coupled simulation-optimization model is applied to find optimal solutions. • Three allocation systems are applied to share the benefits among stakeholders. • Social choice theories support game theory for selecting the best scenario. [ABSTRACT FROM AUTHOR]

[Display omitted] • Multi-objective robust decision-making is demonstrated for urban runoff management. • The SWMM- NSGA-II coupled model generates urban runoff management scenarios. • Alternatives' robustness against deep uncertainty is evaluated under climatic change. • System Vulnerabilities are discovered using PRIM and CART. Decision-makers in urban water systems have long searched for robust runoff management alternatives that can resist deviations from the performance for which they were designed. In this study, with the development of a new comprehensive framework consisting of Many- Objective Robust Decision-Making (MORDM) and Storm Water Management Model (SWMM), robust alternatives for the implementation of Low Impact Development (LID) practices are presented under future climate change. The proposed methodology has been applied to District 10 of the Tehran municipality, located in the central part of Tehran, Iran. In the first step, the performances of General Circulation Models (GCMs) in predicting rainfall in the base period (1986–2005) were compared, and MPI-ESM-LR was selected as the best model for predicting rainfall in the future period (2021–2040). In the next step, by developing the SWMM hydrological model, runoff quality and quantity were simulated under RCP8.5 and RCP2.6. By coupling SWMM with the NSGA-II multi-objective evolutionary algorithm and specifying objective functions, the most optimal LIDs implementation scenarios were produced. Then, well-characterized uncertainties were changed into deep uncertainties, and the robustness of each design alternative was quantified using two robustness assessment methods. Finally, system vulnerabilities were discovered using the patient rule induction method (PRIM) and classification and regression tree (CART). This study showed that scenarios with a higher percentage of LIDs are more robust against deep uncertainties. Using different RCP scenarios also revealed that methodological selection of future climate scenarios can affect the robustness of candidate alternatives. Implementing SWMM-MORDM enables decision-makers to effectively generate runoff management scenarios and visually evaluate their robustness against deep uncertainties. [ABSTRACT FROM AUTHOR]