Optimal Design of Groundwater-Quality Sampling Networks with MOPSO-GS(Case Study: Neyshabour Plain)

Monitoring network optimization is a decision making process for the best combination of available stations. Due to economic considerations and reduction of monitoring costs ، the optimization approach in this study is to reduce monitoring stations without reducing the amount and accuracy of the information obtained. In this study, an optimal design of groundwater quality monitoring network was carried out with the help of an optimization model in the Neishabour plain aquifer. The optimization of the wells network was accomplished by a Multi Objective Particle Swarm Optimization (MOPSO) algorithm. Two objectives containing of minimizing the root mean square error (RMSE) and the number of wells was applied in current research. Kriging interpolation was used for calculating groundwater chlorine concentration values and compared with observation values. As a result of this research was presented a Pareto front exctracted from MOPSO showing the number of wells against their corresponding RMSE, which could be a guide for the design of a groundwater quality monitoring network. The outcome showed that the sampling wells can be reduced to 58 percent with a minimum error increase (all 50 wells in base network with zero error may be reduced to 21 with chlorine concentration error of 13.57 mg/l) in the Neishabour aquifer. Also, the position of these wells was considered as the optimal position.