Combination of geostatistics and self-organizing maps for the spatial analysis of groundwater level variations in complex hydrogeological systems

Successful modelling of the groundwater level variations in hydrogeological systems in complex formations considerably
depends on spatial and temporal data availability and knowledge of the boundary conditions. Geostatistics plays an
important role in model-related data analysis and preparation, but has specific limitations when the aquifer system is
inhomogeneous. This study combines geostatistics with machine learning approaches to solve problems in complex aquifer
systems. Herein, the emphasis is given to cases where the available dataset is large and randomly distributed in the different
aquifer types of the hydrogeological system. Self-Organizing Maps can be applied to identify locally similar input data, to
substitute the usually uncertain correlation length of the variogram model that estimates the correlated neighborhood, and
then by means of Transgaussian Kriging to estimate the bias corrected spatial distribution of groundwater level. The
proposed methodology was tested on a large dataset of groundwater level data in a complex hydrogeological area. The
obtained results have shown a significant improvement compared to the ones obtained by classical geostatistical
approaches.