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The Application of Neural Networks in Predicting Spatial Radiation Environments

Accurately predicting the space radiation environment is crucial for satellite in-orbit management and space science research. A comprehensive neural network model prediction process comprises data analysis, neural network model construction, and the evaluation and validation of model accuracy. Through in-depth data analysis, we can gain a more comprehensive understanding of the distribution, trends, and correlations within the data, providing robust support for selecting a model that best fits the current dataset. Rational construction of the neural network is a key step to ensure the model learns effective information on the training set while performing well on unseen data. Accuracy evaluation also aids in identifying potential overfitting or underfitting issues, guiding further adjustments and improvements to the model. In this paper, we will discuss the application of these key steps in predicting the space radiation environment.