Stakeholder engagement in multi-objective optimization of water quality monitoring network, case study: Karkheh Dam reservoir Mohammad Reza Nikoo, Shokoufeh Pourshahabi, Najmeh Rezazadeh and M. Ehsan Shafiee ABSTRACT Reservoir water quality is important for water quality management downstream. A hierarchical approach is developed to present the monitoring locations within a format that satisfies the objectives of social stakeholders for making final decisions. First, a CE-QUAL-W2 model is applied to simulate water quality variables in the reservoir for a long time using a set of historic data. Second, transinformation entropy theory is used to quantify mutual information among a set of monitoring stations for each water quality variable. Then, a non-dominating sorting genetic algorithm-based model is developed for multi-objective optimization of the water quality monitoring network. Finally, a social choice method is applied to the identified non-dominated solutions to achieve a strategy that is compromised among stakeholders. The variations of the water quality variables at different depths and different seasons are investigated. The proposed approach is illustrated for Karkheh Reservoir in Iran. The number of optimized monitoring stations is the same for all seasons (three out of 22 potential stations) using different social choice methods. The results show the appropriate performance of the proposed methodology for optimization of reservoir water quality monitoring stations. Mohammad Reza Nikoo (corresponding author) Shokoufeh Pourshahabi Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran E-mail: nikoo@shirazu.ac.ir Najmeh Rezazadeh Civil Engineering Faculty, Ferdowsi University, Mashhad, Iran M. Ehsan Shafiee Sensus, Morrisville, NC 27650, USA Key words | CE-QUAL-W2 model, NSGA-II multi-objective optimization method, reservoir monitoring network, social choice, transinformation entropy INTRODUCTION Sampling locations are designed to monitor reservoir water quality. The design of sampling locations is difficult due to a wide range of water quality variables that can be used to pre- sent water quality, the temporal and spatial characteristics of sampling, and the duration and objectives of sampling (Harmancioglu et al. ). For optimization of a water qual- ity monitoring program, various regulations and the physical and geographical properties of reservoirs must be con- sidered in formulating this non-linear, complex problem (Behmel et al. ). The study of the placement of monitoring stations in a reservoir is limited in the literature, although similar studies can be found for identifying locations to monitor water qual- ity in river and groundwater resources. Lee & Kwon () and Lee et al. () proposed methodologies using statistical analysis to reduce redundant sampling locations in a reser- voir. Based on information theory, Lee et al. () proposed an approach for optimization of water quality monitoring stations in a reservoir. They used water quality data at similar depths of Lake Yongdam. Instead of using an optimization approach, they tested all possible combi- nations to find the optimal solution among potential combinations of stations. These studies are not suitable for the large reservoirs, at which the potential sampling 966 © IWA Publishing 2017 Water Science & Technology: Water Supply | 17.4 | 2017 doi: 10.2166/ws.2016.196 Downloaded from https://iwaponline.com/ws/article-pdf/17/4/966/409004/ws017040966.pdf by guest on 16 June 2020