Comparison of genetic algorithms with other methods for the ambient groundwater monitoring network planning

Abstract

This work concentrates on the ambient groundwater monitoring network design. This kind of planning largely concerns itself with constructing a flexible and efficient monitoring network. This study presents a novel procedure that combines genetic algorithms (GAs) with geostatistical theory. The proposed method is compared to other methods that also integrate geostatistical theory with other optimization schemes, including the sequential design method (SDM), branch and bound method (BBM) and non-linear programming method (NPM). These methods are implemented and applied to a simplified field case. The findings indicate that the SDM technique provides a computationally efficient solution for a preliminary study. On the other hand, the multiple choices given by the GAs provide decision-makers with flexibility to consider factors that geostatistics can not.