基于因果分析的机器学习模型在空气质量预报中的构建及评估——以广州市为例OA

To address the increasing challenge of persistent pollution and rising ozone(O₃)levels in Guangzhou,this study developed advanced air quality forecasting models using machine learning techniques.Based on environmental monitoring and meteorological observation data,the Liang-Kleeman information flow was used to conduct causal analysis on factors affecting the concentrations of atmospheric pollutants such as CO,NO2,O3,PM2.5,PM10,and SO2.Using the Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Long Short-Term Memory Neural Network(LSTM)algorithms for integrated modeling,five distinct pollutant concentration forecasting models(RF,XG,LSTM,and the integrated models MIX1 and MIX2)were constructed.These models forecast pollutant concentrations,which were then used to calculate the Air Quality Index(AQI)and identify the primary pollutant.The results show that the integrated models(MIX1 and MIX2)generally outperform the single ones(RF,XGBoost,and LSTM models).For pollutant concentration forecasting,the MIX1 model was optimal for CO,NO2,and O3,while the MIX2 model performed best for PM10,PM2.5,and SO2.For air quality forecasting,the MIX2 model was superior for 1-2 day forecasts,whereas the MIX1 model was optimal for 3-7 day forecasts.The accuracy rates for primary pollutant by the MIX1 and MIX2 models for 1-7 day forecast were 71.26%-83.33%and 73.71%-81.11%,respectively.The models showed high reliability,with accuracy rates for primary pollutant identification ranging from 71.26%to 83.33%for MIX1 and 73.71%to 81.11%for MIX2 across the 1-7 day forecast,providing valuable tools for environmental authorities to implement targeted air pollution control measures.