Optimization of water quality monitoring stations using genetic algorithm, a case study, Sefid-Rud River, Iran

Abstract

Water quality monitoring network needs periodic evaluations based on environmental demands and financial constraints. We used a genetic algorithm to optimize the existing water quality monitoring stations on the Sefid-Rud River, which is located in the North of Iran. Our objective was to optimize the existing stations for drinking and irrigation purposes, separately. The technique includes two stages called data preparation and the optimization. On the data preparation stage, first the basin was divided into four sections and each section was consisted of some stations. Then, the score of each station was computed using the data provided by the water Research Institute of the Ministry of energy. After that, we applied a weighting method by providing questionnaires to ask the experts to define the significance of each parameter. In the next step, according to the scores, stations were prioritized cumulatively. Finally, the genetic algorithm was applied to identify the best combination. The results indicated that out of 21 existing monitoring stations, 14 stations should remain in the network for both irrigation and drinking purposes. The results also had a good compliance with the previous studies which used dynamic programming as the optimization technique.