Multi-objective optimization for combined quality–quantity urban runoff control

Abstract

Abstract. Urban development affects the quantity and quality of urban surface runoff. In recent years, the best management practices (BMPs) concept has been widely promoted for control of both quality and quantity of urban floods. However, means to optimize the BMPs in a conjunctive quantity/quality framework are still under research. In this paper, three objective functions were considered: (1) minimization of the total flood damages, cost of BMP implementation and cost of land-use development; (2) reducing the amount of TSS (total suspended solid) and BOD5 (biological oxygen demand), representing the pollution characteristics, to below the threshold level; and (3) minimizing the total runoff volume. The biological oxygen demand and total suspended solid values were employed as two measures of urban runoff quality. The total surface runoff volume produced by subbasins was representative of the runoff quantity. The construction and maintenance costs of the BMPs were also estimated based on the local price standards. Urban runoff quantity and quality in the case study watershed were simulated with the Storm Water Management Model (SWMM). The NSGA-II (Non-dominated Sorting Genetic Algorithm II) optimization technique was applied to derive the optimal trade off curve between various objectives. In the proposed structure for the NSGA-II algorithm, a continuous structure and intermediate crossover were used because they perform better as far as the optimization efficiency is concerned. Finally, urban runoff management scenarios were presented based on the optimal trade-off curve using the k-means method. Subsequently, a specific runoff control scenario was proposed to the urban managers.

Water Sensitive Urban Design: An Investigation of Current Systems, Implementation Drivers, Community Perceptions and Potential to Supplement Urban Water Services

“…PC SWMM (CHI Software) was selected for continuous modelling of catchment runoff. This model was selected because it has been based on the widely available US EPA SWMM model which has been used in several studies employing continuous simulation of catchment runoff involving WSUD devices [28,29]. It also has useful features in addition to US EPA SWMM which allow the user to quickly and effectively assess model calibration, process long flow time-series and rapidly import data collected in a geographical information system environment.…”

Multiobjective optimization of cluster‐scale urban water systems investigating alternative water sources and level of decentralization

“…Replication across multiple case studies will elucidate general trends from those which are case study dependant. The addition of other criteria, such as maximizing distribution network reliability/resilience [ Todini , ; Shinstine et al ., ], minimizing the change in the stormwater hydrograph from predevelopment status [ Burns et al ., ; Mobley et al ., ], and minimizing pollutant load [ Oraei Zare et al ., ] will enable the understanding of broader trade‐offs in design variables. The use of sensitivity analysis for investigating the impact of discount rates, climate change [ Paton et al ., ], changes in the price of energy [ Wu et al ., ], and the impact in regard to the trade‐off between greenhouse gas emissions and costs based on the mix of energy sources used, and the carbon footprint of these energy sources, will be beneficial [ Stokes et al ., ].…”

Comparing the Selection and Placement of Best Management Practices in Improving Water Quality Using a Multiobjective Optimization and Targeting Method

“…Most of the previous work has focused on using a single objective function which combines both BMP effectiveness and cost [23], sequentially optimizing two objective functions separately [24,25] or optimizing two objective functions of BMP effectiveness and cost simultaneously [22]. Zare et al [28] applied the Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization technique to derive the optimal tradeoff curve simultaneously between three objectives: reducing cost of BMP implementation, maintaining runoff quality, and minimizing runoff volume. Cost of BMPs was estimated based on the volume (Rain barrel and Bio-retention) or the areas (Porous pavement).…”