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• A simulation-optimization (SO) framework is developed in urban water management. • The type, area, and location of LID & DP practices are determined in SO framework. • The urban runoff peak flow and TSS loads are alleviated with LID & DP practices. • A multi-objective, multi-circuit Electimize(MOMCE) algorithm is coupled to SWMM. • The cost-effective & environmentally-friendly alternatives are derived in SO approach. Rapid urbanization and development have led to adverse effects on the urban landscape through flooding and non-point pollutions. Low impact development (LID) techniques, as semi-engineered storm water practices, alleviate urban flooding and pollution. In this study, a simulation-optimization (SO) framework is developed to address the multi-scale, multi-parameter problem of pond and LID sizing and locating. Multi-objective, multi-circuit Electimize (MOMCE) optimization algorithm is developed in Matlab environment and coupled to the storm water management model (SWMM) to provide a comprehensive tool for restoring pre-development hydrological condition, while improving stormwater quality. The optimal layout and sizing of ponds and LID techniques are determined for a spectrum of design storms, including 2-, 5-, 10-, and 50-year events considering peak runoff flow and total suspended solids (TSS) loads. It is shown that increasing the return periods of storm events leads to the increased size of detention ponds and LIDs with storage features. Whereas, larger ponds and LID techniques with longitudinal expansion characteristics are more evident in the TSS Load reduction objective. The research indicates that, for a 10-year storm, renovation of old urban tissues in the study area and proper utilization of LID techniques may result in 33 % and 29.7 % reduction of peak flow and TSS loads, respectively. [ABSTRACT FROM AUTHOR]

Integrated management of quality and quantity of river-reservoir water can provide comprehensive information to manage river-reservoir water resources. However, high computational bottlenecks have prevented such management from being applicable in real-world systems. Accordingly, in the present study, we proposed a multi-objective optimization algorithm based on modular support vector regression (SVR) in which several small sub-SVR modules trained through an efficient adaptive procedure cooperate to solve a large-scale problem related to integrated management of quality and quantity of river-reservoir. The performance of the proposed approach was evaluated through an adaptive surrogate-based simulation-optimization (ASBSO) framework under reservoir selective withdrawal scheme (SWS) in Ilam integrated River-Reservoir. The ASBSO framework provided a set of non-dominated optimal solutions to alleviate Ilam River water quality standard violations, enhance Ilam Reservoir outflow water quality, and maximize the downstream water supply satisfaction. The analysis of the Pareto-front indicated that the implementation of MPWLA (multi-pollutant waste load allocation) programs at any level could alleviate the water quality problems in Ilam Reservoir. Furthermore, the reservoir water storage in the regular patterns to meet downstream water demands have resulted in water quality deteriorations in Ilam Reservoir. The results obtained from examining the proposed approach, showed that integrated river-reservoir system management improved water quality in the range from 7 to 28% at the checkpoints of the Gol-Gol Branch of Ilam River and in the range from 5 to 21% at Ilam Reservoir outflow. [Display omitted] • Integrated waste load allocation program & reservoir operation strategy are presented. • The eutrophication & water supply management model in river-reservoir is developed. • A multi-objective optimization algorithm based on adaptive modular SVR is applied. • A modular SVR, consists of several small sub-SVR modules, depicts water quality. • Water quality in the river-reservoir system and downstream water supply are enhanced. [ABSTRACT FROM AUTHOR]