耦合遗传算法与RBF神经网络的PM_2.5浓度预测模型

We developed a coupling model combining the radial basis function (RBF) artificial neural network and the genetic algorithm to predict the average PM_2.5 concentrations in Beijing in the next 24hours. This model mainly used air pollutant concentration data obtained by air quality monitoring stations as inputs, and relied on the genetic algorithm to determine parameters such as the number of hidden layer neurons and the spread constant. The model had a good prediction performance (R-square up to 0.75) with less data inputs because it does not need meteorological or geographical information for its training process. Further improvements can be made by using multi-source data and increasing sample size in the training process to enhance the accuracy and robustness of the model for the prediction of air pollution in different situations.