ABSTRACT
When astronauts are outside Earth's protective magnetosphere, they are subject to large radiation doses resulting from solar particle events. The total dose received from a major solar particle event in deep space could cause severe radiation poisoning. The dose is usually received over a 20-40 h time interval but the event's effects may be reduced with an early warning system. This paper presents a method to predict the total dose early in the event. It uses a locally weighted regression model, which is easier to train, and provides predictions as accurate as the neural network models that were used previously.
Subject(s)
Algorithms , Artificial Intelligence , Radiation Protection/methods , Radiometry/methods , Risk Assessment/methods , Solar Activity , Computer Simulation , Cosmic Radiation , Humans , Linear Energy Transfer , Materials Testing , Models, Statistical , Neural Networks, Computer , Radiation Dosage , Radiation Injuries/prevention & control , Regression Analysis , Risk Factors , Scattering, Radiation , Space FlightABSTRACT
When astronauts are outside earth's protective magnetosphere, they are subject to large radiation doses resulting from solar particle events (SPEs). The total dose received from a major SPE in deep space could cause severe radiation poisoning. The dose is usually received over a 20-40 h time interval but the event's effects may be mitigated with an early warning system. This paper presents a method to predict the total dose early in the event. It uses a locally weighted regression model, which is easier to train and provides predictions as accurate as neural network models previously used.
