Special issue: geostatistics and machine learningSpecial issue: geostatistics and machine learning Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2024-02-262022enRecent years have seen a steady growth in the number of papers that apply machine learning methods to problems in the earth sciences. Although they have different origins, machine learning and geostatistics share concepts and methods. For example, the kriging formalism can be cast in the machine learning framework of Gaussian process regression. Machine learning, with its focus on algorithms and ability to seek, identify, and exploit hidden structures in big data sets, is providing new tools for exploration and prediction in the earth sciences. Geostatistics, on the other hand, offers interpretable models of spatial (and spatiotemporal) dependence. This special issue on Geostatistics and Machine Learning aims to investigate applications of machine learning methods as well as hybrid approaches combining machine learning and geostatistics which advance our understanding and predictive ability of spatial processes.http://creativecommons.org/licenses/by/4.0/Mathematical Geosciences543459–465De Iaco_et_al_Math. Geosci._54(3)_2022.pdfChania [Greece]Library of TUC2024-02-26application/pdf205.2 kBfree De Iaco Sandra Christopoulos Dionysios Χριστοπουλος Διονυσιος Lin Guang Springer Geostatistics Statistical learning Machine learning Spatial process Gaussian process regression