A turbulence prediction model based on sparrow search algorithm optimized BP artificial neural network(SSA-BP) has been proposed. Firstly ,the BP artificial neural network is adopted as the basic framework for turbulence prediction models. By collecting and processing meteorological factors such as temperature ,humidity ,and wind speed ,they are used as input layer features. Then ,the sparrow search algorithm is used to optimize the weights and biases of the BP artificial neu-ral network. To verify the effectiveness of this method ,experiments were conducted using atmospheric turbulence data and meteorological data from ground meteorological stations. The experimental results indicate that the SSA-BP artificial neural network can successfully predict the development trend of atmospheric turbulence ,and has high prediction accuracy and sta-bility. Being able to fully utilize the nonlinear features in atmospheric turbulence data provides strong support for turbulence prediction research and practical applications.
关键词
BP人工神经网络/麻雀搜索算法/气象参数/大气湍流预测
Key words
BP artificial neural network/sparrow search algorithm/seteorological parameters/atmospheric turbulence prediction
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