Machine learning analysis for predicting spatial distribution and key influencers of stable isotope patterns in European precipitation

摘要: Natural abundance variations in stable isotope ratios of hydrogen and oxygen are important environmental tracers with a significant range of applications (eg, the exploration of the present water cycle, paleoclimate reconstructions, ecology, and food authenticity). These applications and research themes are often based on spatially explicit predictions of precipitation isotopic variations obtained from point sample collections and measurements through various interpolation techniques. The derivation of spatially continuous and georeferenced isotope databases, known as isotopic landscapes (isoscapes), has been considered most effective through regression kriging for precipitation beginning in the early 2000s. However, the number of interpolation methods used in geostatistics has increased rapidly in recent decades, with new machine learning algorithms becoming increasingly important and proving more …