Optimization of groundwater monitoring network using Ant Colony Optimization (ACO) algorithm

In this paper, using the one of the most robust optimization technique, Ant Colony Optimization Algorithm (ACO) the minimum number of required sampling points was determined in Hashtgerd plain. The ACO technique is based on the minimum distance between the food source and the ant nest. In Hashtgerd plain using ACO about 30% of sampling point were reduced. In this aquifer, the number of sampling points for contamination research where in 25 location and after the application of ACO technique results showed that only 18 sampling points is enough and the 7 number of sampling points is not necessary and introduced more expenses for the contamination study. In addition, the results of nitrate contamination contour plot before and after reducing the sampling points from 25 to 18 shows a small change in contour maps. The maximum RMSE after reduction 7 sampling points is about 0.3198 that shows the minimum error for optimized network.