为了提升挥发性有机物(Volatile Organic Components，VOCs)的预测精度，在反向传播(Back-Propagation，BP)网络结构的基础上使用优化算法分别为遗传算法(Genetic Algorithms，GA)优化 BP 神经网络(GA-BP)和粒子群算法(Particle Swarm Optimization，PSO)优化 BP 神经网络(PSO-BP)对VOCs质量浓度进行预测。首先，对污染物及气象因子进行筛选。采用相关性分析法及逐步回归法进行分析筛选，并筛选出合适的输入变量。其次，建立BP神经网络结构。利用BP、GA-BP、PSO-BP神经网络，以石家庄市2022年夏季污染数据为样本对VOCs质量浓度进行预测。结果显示，经相关性分析及逐步回归法筛选，将PM2。5质量浓度、O3质量浓度、NO2质量浓度、温度、相对湿度作为输入变量。经预测结果对比，PSO-BP神经网络模型的预测精度较高，烷烃、烯烃、芳香烃和含氧烃实测值与预测值之间的拟合程度(R2)分别为0。80、0。55、0。78、0。67。研究结果可为日后VOCs污染预报预警提供理论参考。
Prediction model of VOCs in Shijiazhuang based on GA/PSO-BP neural network
To improve the accuracy of Volatile Organic Compounds(VOCs)prediction in Shijiazhuang region,based on the BP neural network algorithm,this study optimizes it with Genetic Algorithm(GA)and Particle Swarm Optimization(PSO).This study utilizes data from the summer of 2022 and preprocesses it by categorizing it into Alkanes,Alkenes,Alkynes,Aromatic hydrocarbons,Halogenated hydrocarbons,and Oxygenated Volatile Organic Compounds(OVOCs).The emission inventory identifies several VOCs components highly affected by OFP,including Alkanes,Alkenes,Aromatic hydrocarbons,and OVOCs.Missing values are handled using two methods:numerical interpolation for datasets with fewer missing values,and modeling interpolation for datasets with more missing values.Significant variables that influence VOCs including PM2.5,NO2,O3,temperature,and Relative Humidity(RH),are filtered through both correlation analysis and stepwise regression modeling.These influential factors are used as input variables,applying three neural network structures:BP,GA-BP,and PSO-BP to predict the 24-hour concentration of different types of VOCs on a particular day,allowing for a comparison with the actual values.By observing the curves between the predicted and actual values,along with comparing the errors and goodness of fit among the three networks,the results show that PSO-BP reduces the mean square errors(I MSE)by 69.40％,37.84％,57.38％,and 49.88％compared to BP,and 63.95％,34.29％,42.29％,and 46.72％compared to GA-BP when predicting Alkanes,Alkenes,Aromatic hydrocarbon,and OVOCs,respectively.Compared to BP,GA-BP reduces the mean square errors(IMSE)by 15.11％,5.40％,26.16％,and 5.93％,respectively.Through analysis,the optimized network structure overcomes the limitation of poor global search capability found in a single BP network,and its advantage over the GA algorithm lies in the memory function of the PSO algorithm.As a result,PSO-BP holds promising prospects for predicting VOCs during the summer season.
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