基于神经网络的西安市PM2.5预测分析

Prediction and analysis of PM2.5 in Xi’an based on neural network

选取西安市2018年除PM2.5外其余主要污染物(PM10、NO2、CO、O3、SO2)的日均数据以及气压、温度、湿度、风速四项气象日均数据作为神经网络的输入层,PM2.5的日均浓度作为输出层,建立多层感知器神经网络预测模型。通过对神经网络模型的测试,发现当隐含层数为8时,预测模型对于PM2.5预测精度较高,并且通过外推预测检验验证了此模型的可行性,如能全面考虑影响PM2.5浓度变化的其余因素,选择更为合适的数据集学习测试,能够再次提高模型的预测精度。

The daily average data of other major pollutants(PM10,NO2,CO,O3,SO2)except PM2.5 in Xi’an in 2018,as well as the daily average data of air pressure,temperature,humidity and wind speed,were selected as the input layer of neural network and PM2.5 as multi-layer perceptual to establish the neural network prediction model.Through the test of the neural network model,it is found that when the hidden layer number is 8,the prediction model is higher for the PM2.5 prediction accuracy,and the feasibility of the model is verified by the extrapolation and prediction test,and the remaining factors that influence the change of the concentration of the PM2.5 can be fully considered,The better data set learning test can be selected,and the prediction accuracy of the model can be improved again.