Conflict resolution in the multi-stakeholder stepped spillway design under uncertainty by machine learning techniques

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A Novel Dynamic Hydrant Flushing Framework Facilitated by Categorizing Contamination Events

Security of water distribution systems is of highest importance to public health. In this study, a novel dynamic hydrant flushing strategy is developed to remove the injected contamination mass in the shortest possible time after being detected by the sensors. The coupled EPANET-IHS simulation-optimization model is developed to determine the hydrant flushing strategies (the hydrant sets and their opening/closing time). This allows water utility managers to utilize the hydrants instantly after the sensor alarm with no need for excessive investigations, which enhances the flushing effectiveness. Developed based on thousands of contaminant injection scenarios (contamination events), the proposed hydrant flushing strategies are reliable to remove any injected contamination mass. Moreover, the operation of hydrant sets is dynamically validated by closing/opening of hydrants throughout the total hydrant flushing duration, which enables the model to flush up to more 20%. The methodology is examined on the mid-sized WDS of Mesopolis city

A Stochastic Conflict Resolution Optimization Model for Flood Management in Detention Basins: Application of Fuzzy Graph Model

Floods are a natural disaster of significant concern because of their considerable damages to people’s livelihood. To this extent, there is a critical need to enhance flood management techniques by establishing proper infrastructure, such as detention basins. Although intelligent models may be adopted for flood management by detention basins, there is a literature gap on the optimum design of such structures while facing flood risks. The presented study filled this research gap by introducing a methodology to obtain the optimum design of detention basins using a stochastic conflict resolution optimization model considering inflow hydrographs uncertainties. This optimization model was developed by minimizing the conditional value-at-risk (CvaR) of flood overtopping, downstream flood damage, and deficit risk of water demand, as well as the deviation of flood overtopping and downstream damage based on non-linear interval number programming (NINP), for four different outlets types via a robust optimization tool, namely the non-dominated sorting genetic algorithm-III (NSGA-III). Conflict resolution was performed using the graph model for conflict resolution (GMCR) technique, enhanced by fuzzy preferences, to comply with the authorities’ priorities. Results indicated that the proposed framework could effectively design optimum detention basins consistent with the regional and hydrological standards

Design of a High-Coverage Ground-Based CO\u3csub\u3e2\u3c/sub\u3e Monitoring Layout Using a Novel Information Theory-Based Optimization Model

Over the past decade, monitoring of the carbon cycle has become a major concern accented by the severe impacts of global warming. Here, we develop an information theory-based optimization model using the NSGA-II algorithm that determines an optimum ground-based CO2 monitoring layout with the highest spatial coverage using a finite number of stations. The value of information (VOI) concept is used to assess the efficacy of the monitoring stations given their construction cost. In conjunction with VOI, the entropy theory—in terms of transinformation—is adopted to determine the redundant (overlapping) information rendered by the selected monitoring stations. The developed model is used to determine a ground-based CO2 monitoring layout for Iran, the eighth-ranked country emitting CO2 worldwide. An NSGA-II optimization model provides a tradeoff curve given the objectives of (1) minimizing the size of monitoring network; (2) maximizing VOI, i.e., spatial coverage; and (3) minimizing transinformation, i.e., overlapping information. Borda count method is then employed to select the most appropriate compromise monitoring layout from the Pareto-front solutions given regional priorities and concerns