Abstract
In this study, a multilayer perceptron model is implemented for predicting meteorological values. Based on the known distribution of meteorological values for several previous days, the task was set to predict the values of the near ground air temperature. The overall mean square error for the entire forecast was 3.11 C. Comparison of various optimization methods showed the advantage of the method of Adaptive Moment Estimation. Comparison of the multilayer perceptron model forecasting results with the Weather Research and Forecasting numerical model forecast showed the promise of using neural networks to predict meteorological parameters at weather observation points.
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