AIMC Topic: Uranium

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Multi-source machine learning and spaceborne remote sensing data accurately predict three-dimensional soil moisture in an in-service uranium disposal cell.

Journal of environmental management Aug 31, 2024
One reason arid and semi-arid environments have been used to store waste is due to low groundwater recharge, presumably limiting the potential for meteoric water to mobilize and transport contaminants into groundwater. The U.S. Department of Energy O...
Machine Learning Soil Groundwater Environmental Monitoring Remote Sensing Technology Uranium
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Radon potential mapping in Jangsu-gun, South Korea using probabilistic and deep learning algorithms.

Environmental pollution (Barking, Essex : 1987) Oct 18, 2021
The adverse health effects associated with the inhalation and ingestion of naturally occurring radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas. This study detected radon-prone areas using a geog...
Algorithms Deep Learning Radiation Monitoring Uranium Air Pollutants, Radioactive Radon
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Evaluation of the suitability of neural network method for prediction of uranium activity ratio in environmental alpha spectra.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine Aug 15, 2015
Applying Artificial Neural Network to an alpha spectrometry system is a good idea to discriminate the composition of environmental and non-environmental materials by the estimation of the (234)U/(238)U activity ratio. Because it eliminates limitation...
Humans Neural Networks, Computer Environmental Pollutants Spectrum Analysis Monte Carlo Method Alpha Particles Radioactive Pollutants Uranium
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