Optimal location of water quality monitoring stations in river systems by discrete transinformation entropy

In this study, optimization of location of sampling stations for water quality of river system using information entropy theory in Lavasanat and Tehran-Karaj catchments with 11 sampling stations for 12 water quality and quantity variables for a statistical period of 21 years has been considered. First, using the discrete transinformation entropy theory, a T-D diagram was drawn for each variable, which indicates the distance between stations and transinformation. Then, using analytical hierarchical process (AHP) method and according to water consumption for agriculture and drinking, the weight of all variables was determined using Expert Choice software. The optimal distance between monitoring stations was obtained based on the total weight of each qualitative variable at its optimal distance of 14.1 km. Also, the optimal distance was calculated by fuzzy AHP method, which was 13.83 km. Then different scenarios were performed to eliminate or increase the number of sampling stations. The results show the efficiency of discrete entropy theory in combination with multi-criteria decision-making method in optimizing the number and distribution of water quality monitoring stations in river systems.