Print Email Facebook Twitter Combination of geostatistics and self-organizing maps for the spatial analysis of groundwater level variations in complex hydrogeological systems Title Combination of geostatistics and self-organizing maps for the spatial analysis of groundwater level variations in complex hydrogeological systems Author Varouchakis, Emmanouil A. (Technical University of Crete) Solomatine, D.P. (TU Delft Water Resources; IHE Delft Institute for Water Education) Corzo Perez, Gerald A. (IHE Delft Institute for Water Education) Jomaa, Seifeddine (Helmholtz Centre for Environmental Research - UFZ) Karatzas, George P. (Technical University of Crete) Date 2023 Abstract 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. Subject Box-CoxGeostatisticsGroundwaterMachine learningSelf-organizing mapsTransgaussian Kriging To reference this document use: http://resolver.tudelft.nl/uuid:b110b256-3e5c-4a32-9406-4e899e54468c DOI https://doi.org/10.1007/s00477-023-02436-x ISSN 1436-3240 Source Stochastic Environmental Research and Risk Assessment, 37 (8), 3009-3020 Part of collection Institutional Repository Document type journal article Rights © 2023 Emmanouil A. Varouchakis, D.P. Solomatine, Gerald A. Corzo Perez, Seifeddine Jomaa, George P. Karatzas Files PDF s00477_023_02436_x.pdf 1.36 MB Close viewer /islandora/object/uuid:b110b256-3e5c-4a32-9406-4e899e54468c/datastream/OBJ/view