| 摘要: |
| 为有效进行城市空气质量预测、推进城市空气污染防治, 弥补传统统计学模型在大数据时代背景下对城市空气质量预测准确率低、容错能力差等问题，提出利用随机森林回归构建城市空气质量预测模型;综合考量污染物浓度、气象参数、时间参数等多方面影响因素，通过网格搜索法调整参数的最优组合，构建基于随机森林回归算法的城市空气质量预测模型;基于重庆市2017-01-01—2020-07-31的指标数据，对重庆市空气质量进行预测分析，结果表明:在模型下训练集与测试集的确定性系数R2均在99%以上，均方误差DMSE和平均绝对误差D MAE在训练集和测试集上的取值均在可接受范围内，证实模型具有运行速度快、预测误差小、具有较高的预测精度等优点，具备较好的学习能力与泛化能力。 |
| 关键词: 随机森林回归 空气质量预测 气象参数 空气质量指数 |
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| Urban Air Quality Prediction Model Based on Random Forest Regression and Meteorological Parameters：Take Chongqing as an Example |
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XU Yan-ping，CHEN Yi-an
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(School of Mathematics and Statistics,Chongqing Technology and Business University,Chongqing 400067,China
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| Abstract: |
| In order to effectively predict urban air quality, promote urban air pollution prevention and control, and make up for the deficiency of low accuracy and poor fault tolerance of traditional statistical models for urban air quality prediction under the background of big data era, a prediction model of urban air quality based on Stochastic Forest regression is proposed. Considering the pollutant concentration, meteorological parameters, time parameters and other factors, the optimal combination of parameters was adjusted by grid search method, and the urban air quality prediction model based on Stochastic Forest regression algorithm was established. Based on the index data of Chongqing from January 1, 2017 to July 31, 2020, the air quality in Chongqing is predicted and analyzed. The results show that the certainty coefficients of training set and test set are above 99%, and the mean square error and average absolute error under the model on the training set and test set are within the acceptable range, which proves that the model has the advantages of fast running speed, small prediction error, high prediction accuracy, and good learning ability and generalization ability. |
| Key words: random forest regression air quality prediction meteorological parameters air quality index |
