A decision support tool for optimising groundwater-level monitoring networks using an adaptive genetic algorithm

Abstract

Mapping of the spatial variability of sparse groundwater-level measurements is usually achieved by means of geostatistical methods. This work tackles the problem of deficient sampling of an aquifer, by employing an innovative integer adaptive genetic algorithm (iaGA) coupled with geostatistical modelling by means of ordinary kriging, to optimise the monitoring network. Fitness functions based on three different errors are used for removing a constant number of boreholes from the monitoring network. The developed methodology has been applied to the Mires basin in Crete, Greece. The methodological improvement proposed concerns the adaptive method for the GA, which affects the crossover–mutation fractions depending on the stall parameter, aiming at higher accuracy and faster convergence of the GA. The initial dataset consists of 70 monitoring boreholes and the applied methodology shows that as many as 40 boreholes can be removed, while still retaining an accurate mapping of groundwater levels. The proposed scenario for optimising the monitoring network consists of removing 30 boreholes, in which case the estimated uncertainty is considerably smaller. A sensitivity analysis is conducted to compare the performance of the standard GA with the proposed iaGA. The integrated methodology presented is easily replicable for other areas for efficient monitoring networks design. HIGHLIGHTS Development of an innovative adaptive genetic algorithm for optimising groundwater-level monitoring networks.; Coupling of evolutionary algorithms with geostatistics for monitoring network optimisation.; Development of a monitoring network design optimisation tool, easily applicable to any area, which considerably reduces sampling efforts, while achieving accurate mapping of groundwater levels.;

Keywords

• adaptive genetic algorithm
• geostatistical modelling
• groundwater monitoring network optimisation
• kriging-based genetic algorithm optimisation