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Deep saline formations are considered as potential sites for geological carbon storage (GCS). To better understand the CO2 trapping mechanism in saline aquifers, it is necessary to develop robust tools to evaluate CO2 trapping efficiency. This paper introduces the application of Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF) to predict CO2 trapping efficiency in saline formations. First, the uncertainty variables, including geologic parameters,doi:10.21203/rs.3.rs-587644/v1 fatcat:jmwoqfo7wrgl3j4rvit5llcjzu