Abstract
In this work, aiming at the problem of missing element values in real-time meteorological data, we propose a radial basis function (RBF) neural network model based on rough set to optimize the analysis and prediction of meteorological data. In this model, the relative humidity of a single station is taken as an example, and the meteorological influencing factors are reduced by rough set theory. The key factors are used as the input of RBF neural network to interpolate the missing data. The experimental results show that the interpolation effect of the model is significantly higher than that of the linear interpolation method, which provides an effective processing method for the lack of real-time meteorological data, and improves the analysis and prediction effect of meteorological data.
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