【目的】比较分析XGBoost模型、LightGBM模型、随机森林模型(RF)、K最近邻模型(KNN)、长短期记忆神经网络(LSTM)、决策树模型(DT)共6个PM_2.5浓度预测模型,以准确、及时预测环境PM_2.5浓度。【方法】基于重庆市合川区2020年全年空气质量监测数据和气象数据,通过最大相关最小冗余算法(MRMR)进行数据降维选择最优特征子集,作为模型的输入,逐一进行PM_2.5浓度预测;考虑到不同季节PM_2.5浓度差异较大,故分季节预测了PM_2.5浓度;为了探究各模型预测性能,计算了各模型运行时间和内存占用,并基于PM_2.5与特征变量的相关性和特征变量的重要性探讨了模型预测性能季节性差异原因。【结果】模型总体预测精度从高到低排序为 XGBoost、RF、LightGBM、LSTM、KNN、DT模型;预测性能方面,6个模型均表现为秋冬季节预测精度高于春夏季节;LightGBM模型可在保证模型精度的情况下,大幅减少模型训练时间和内存占用;特征重要性显示PM₁₀浓度、气温和气压的重要性高,O₃浓度、风向和NO₂浓度重要性相对较弱。【结论】采取MRMR方法进行数据降维选取的最优特征子集能较好地预测PM_2.5浓度;相比较而言,XGBoost、RF、LightGBM、LSTM模型在PM_2.5浓度预测上具有较优性能,其中综合性能较好的为LightGBM模型。

【Objective】Comparative analysis of the XGBoost model, LightGBM model, random forest model (RF), K nearest neighbor model (KNN), long short-term memory neural network (LSTM), and the decision tree model (DT), a total of six PM_2.5 concentration prediction models was undertaken to ensure accurate and timely prediction of the ambient PM_2.5 concentration.【Method】Based on a full-year of air quality monitoring data and the meteorological data of Hechuan District, Chongqing City in 2020, the maximum minimum redundancy algorithm (MRMR) was used to reduce the data dimensionality to select the optimal feature subset, which is used as the model input. The PM_2.5 concentration prediction was then undertaken one at a time. Considering that the PM_2.5 concentration varies considerably during different seasons, the PM_2.5 concentration was predicted according to season. This was undertaken to explore the prediction performance of each model and the running time and memory usage of each model were calculated. Based on the correlation between PM_2.5 and the characteristic variables and the importance of the characteristic variables, the causes of the seasonal differences in model prediction performance are discussed.【Result】The overall prediction accuracy of the model is ranked as XGBoost, RF, LightGBM, LSTM, KNN, and the DT models. In terms of the prediction performance, the six models all show that the prediction accuracy in autumn and winter is higher than that of spring and summer. The LightGBM model can considerably reduce the training time and memory occupation of the model while ensuring the model accuracy. The importance of these features shows that the importance of PM₁₀, temperature, and the air pressure is high, while the importance of O₃, wind direction, and NO₂ is relatively weak.【Conclusion】The optimal feature subset selected using the MRMR method for data dimensionality reduction can better predict the PM_2.5 concentration. In comparison, the XGBoost, RF, LightGBM, and the LSTM models have higher performance in PM_2.5 prediction, among them, the Light GBM has better comprehensive performance.