Search

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]

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]