首页|Research Results from China University of Geosciences Update Understanding of Machine Learning (Machine Learning-Based Uranium Prospectivity Mapping and Model Explainability Research)
Research Results from China University of Geosciences Update Understanding of Machine Learning (Machine Learning-Based Uranium Prospectivity Mapping and Model Explainability Research)
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Current study results on artificial intelligence have been published. According to news originating from Beijing, People's Republic of China, by NewsRx editors, the research stated, "Sandstonehosted uranium deposits are indeed significant sources of uranium resources globally." Financial supporters for this research include Ministry of Science And Technology of The People's Republic of China. The news journalists obtained a quote from the research from China University of Geosciences: "They are typically found in sedimentary basins and have been extensively explored and exploited in various countries. They play a significant role in meeting global uranium demand and are considered important resources for nuclear energy production. Erlian Basin, as one of the sedimentary basins in northern China, is known for its uranium mineralization hosted within sandstone formations. In this research, machine learning (ML) methodology was applied to mineral prospectivity mapping (MPM) of the metallogenic zone in the Manite depression of the Erlian Basin. An ML model of 92% accuracy was implemented with the random forest algorithm. Additionally, the confusion matrix and receiver operating characteristic curve were used as model evaluation indicators."
China University of GeosciencesBeijingPeople's Republic of ChinaAsiaActinoid Series ElementsCyborgsEmerging TechnologiesMachine LearningUranium
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