Aerosol plays a very important role in affecting the earth-atmosphere radiation budget, and particle size distribution is an important aerosol property parameter. Therefore, it is necessary to determine the particle size distribution. However, the particle size distribution determined by the particle extinction efficiency factor according to the Mie scattering theory is an ill-conditioned integral equation, namely, the Fredholm integral equation of the first kind, which is very difficult to solve. To avoid solving such an integral equation, the BP neural network prediction model was established. In the model, the aerosol optical depth obtained by sun photometer CE-318 and kernel functions obtained by Mie scattering theory were used as the inputs of the neural network, particle size distributions collected by the aerodynamic particle sizer APS 3321 were used as the output, and the Levenberg–Marquardt algorithm with the fastest descending speed was adopted to train the model. For verifying the feasibility of the prediction model, some experiments were carried out. The results show that BP neural network has a better prediction effect than that of the RBF neural network and is an effective method to obtain the aerosol particle size distribution of the whole atmosphere column using the data of CE-318 and APS 3321.

中文翻译：

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基于神经网络的气溶胶粒径分布预测

气溶胶在影响地球大气辐射预算方面起着非常重要的作用，而粒径分布是重要的气溶胶特性参数。因此，有必要确定粒度分布。然而，根据米氏散射理论由颗粒消光效率因子确定的粒径分布是病态积分方程，即第一类弗雷德霍姆积分方程，很难求解。为避免求解此类积分方程，建立了BP神经网络预测模型。在该模型中，将太阳光度计CE-318获得的气溶胶光学深度和米氏散射理论获得的核函数用作神经网络的输入，由空气动力学粒度仪APS 3321收集的粒度分布用作输出，并采用降速最快的Levenberg-Marquardt算法训练模型。为了验证预测模型的可行性，进行了一些实验。结果表明，BP神经网络比RBF神经网络具有更好的预测效果，并且是使用CE-318和APS 3321数据获得整个大气柱气溶胶粒径分布的有效方法。