Gaussian process machine learning and Kriging for groundwater salinity interpolation. (October 2021)

• Record Type: Journal Article
• Authors: Cui, Tao; Pagendam, Dan; Gilfedder, Mat
• Is Part Of: Environmental modelling & software. Volume 144(2021)
• Publication Date: 2021-10
• Subjects: Groundwater salinity -- Airborne electromagnetic (AEM) -- Musgrave Province Australia -- Cokriging; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105170
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Gaussian processes (GPs) provide statistically optimal predictions in the sense of unbiasedness and maximal precision. Although the modern implementation of GPs as a machine learning technique is more capable and flexible than Kriging, their employment in environmental science is less routine. Their flexibility and capability as a spatial data interpolation technique are demonstrated by applying them to groundwater salinity prediction in a data-sparse region in Australia. By learning from multiple data sources, including AEM and DEM data, GPs have generated groundwater salinity maps with rich local details and quantified uncertainty to support risk-based decision making. The results demonstrate the great worth of nonpoint data with regional spatial coverage to provide more realistic heterogeneity in aquifer properties that are critical for many studies such as contaminant transport. GPs should be further encouraged in groundwater science for data interpolation and prediction, especially when point measurements are sparse and multiple predictors are available. Highlights: Gaussian Processes were used for groundwater salinity interpolation. GPs are easier than Kriging for including multiple secondary variables. AEM data are valuable for mapping local variations of groundwater salinity. GPs should be used more routinely as an alternative to Kriging.