Optimal Design of Obsevation Wells in a Groundwater Monitoring Network Using Meta-Heuridtic Genetic Algorithm

Well designed monitoring networks are essential for the effective management of groundwater resources but the costs of monitoring well installations and sampling can prove prohibitive. The challenge is to obtain adequate water quality and quantity information with a minimum number of wells and sampling points, a task that can be approached objectively and effectively using numerical optimization methods. Unfortunately, aquifer systems tend to be complex and monitoring can be very expensive, particularly when it requires the installation of a dedicated network of monitoring wells. In recent years, the challenge has been to design monitoring networks that are both efficient and cost effective. With regards to groundwater monitoring systems, where the challenge is to maximize the availability of good quality data while minimizing the number of sampling sites and thereby limiting costs, optimization techniques clearly have a potentially valuable application. In this paper we use a site in northern Iran to test the ability of GA, when used in combination with Kriging, in comparison with PSO to lower the cost of a monitoring network by reducing the number of monitoring wells without compromising the quality of the interpolated data. The results of the optimization showed that the number of observation wells in the Astaneh aquifer monitoring network could be reduced by 26% from 57 to 42 without a significant loss of information. The root mean square error (RMSE) for the final optimized network in GA was 0.2025 m, that in PSO 0.3222 m. A comparison of RMSE values determined using the GA algorithm with those calculated using PSO algorithm technique showed good agreement and provides strong support for more efficient GA approach.