基于多算法融合与人文特征的兰州市PM_(2.5)等级预测方法

Prediction of PM_(2.5)Level in Lanzhou City Based on Multi-algorithm Fusion and Human Feature

PM_(2.5)对空气质量影响较大,相较于较粗的大气颗粒物对人体健康危害更大。为了便于准确预测PM_(2.5)浓度等级,提出一种基于多算法融合与人文特征的PM_(2.5)浓度等级预测方法。首先,选取兰州市2018-2021年的气象数据、预测站点PM_(2.5)浓度随时间变化的规律、周围站点的时空关联性作为特征因子;然后分别通过灰色关联度分析法和皮尔逊系数分析气象指标的强关联度与各气象因子与污染物的关联度,并利用时间维度将其扩充为多个特征(人文特征);接下来基于多层感知机、随机森林、LSTM等算法建立模型预测PM_(2.5)等级;最后基于多特征融合算法利用各模型的预测结果构建最终预测模型。实验表明,所提方法预测PM_(2.5)等级的精确度达到89%,准确率与F值达到88%,对预测空气质量与制定相关预防措施具有重要意义。

PM_(2.5)has a significant impact on air quality and poses a greater threat to human health compared to coarser atmospheric particles.To facilitate accurate prediction of PM_(2.5)concentration levels,PM_(2.5)concentration level prediction method based on multi algorithm fusion and humanistic features is proposed.Firstly,meteorological data from Lanzhou City from 2018 to 2021,the temporal variation of PM_(2.5)concentra⁃tion at predicted stations,and the spatiotemporal correlation of surrounding stations were selected as characteristic factors;Then,the strong correlation degree of meteorological indicators and the correlation degree of various meteorological factors and pollutants were analyzed using grey correlation analysis and Pearson coefficient,respectively,and expanded into multiple features(humanistic features)using the time di⁃mension;Next,based on multi-layer perceptron,random forest,LSTM and other algorithms,a model is established to predict PM_(2.5)level;Fi⁃nally,based on the multi feature fusion algorithm,the final prediction model is constructed using the prediction results of each model.The ex⁃periment shows that the accuracy of the proposed method in predicting PM_(2.5)level reaches 89%,and the accuracy and F-value reach 88%,which is of great significance for predicting air quality and formulating relevant preventive measures.