Optimization of groundwater quality monitoring network using information theory and simulated annealing algorithm

In this paper a methodology of assessment and optimization of groundwater quality monitoring network which takes into account the evaluation criteria derived from the Shannon information theory is presented. The fundamental criteria derived from this theory are: (1) the value of marginal information entropy, which is a measure of the amount of information containing in the data in a location of sampling point, and (2) the value of transinformation (mutual information) which measures the amount of information shared between each of two sampling points. Transinformation can be interpreted as an index of the stochastic dependence between the random variables corresponding to groundwater quality data recorded in different sampling points of monitoring network and shows the reduction of uncertainty included in one variable due to the knowledge of the other variable. In the optimization problem the objective function involving the value of transinformation of the investigated water quality parameters (Cl, Cu, Na) is minimized. To minimize the objective function the simulated annealing algorithm, which allows to find a satisfactory sub-optimal solution, was used. The proposed methodology was applied to optimize the groundwater monitoring network of contaminant reservoir Żelazny Most, one of the worlds biggest industrial waste disposal site, which collects post-flotation contaminants originating from copper ore treatment. The results show an increase in the effectiveness of the monitoring network by reducing the number of sampling points while maintaining an acceptable amount of information available in the network.