Fusion of geostatistics and machine learning under a stochastic approach for the spatial analysis of groundwater level variations

Successful modelling of the groundwater level variations in hydrogeological systems of 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. In this research work, we show how the fusion of geostatistics with machine learning can solve some of these problems in complex aquifer systems, mainly when 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 district, and the results were significantly improved if compared to classical geostatistical approaches.

The research project is implemented in the framework of H.F.R.I call “Basic research Financing (Horizontal support of all Sciences)” under the National Recovery and Resilience Plan “Greece 2.0” funded by the European Union – NextGenerationEU (H.F.R.I. Project Number: 16537).