| 季节指数改进的PM2.5质量浓度组合预测模型研究 |
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| 引用本文： | 曾江毅,李志生,欧耀春,金宇凯.季节指数改进的PM2.5质量浓度组合预测模型研究[J].广东工业大学学报,2022,39(3):89-94. |
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| 作者姓名： | 曾江毅 李志生 欧耀春 金宇凯 |
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| 作者单位： | 广东工业大学 土木与交通工程学院, 广东 广州 510006 |
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| 基金项目： | 广东省自然科学基金资助项目 |
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| 摘 要： | 随着我国的经济和城市化迅速发展，PM2.5主导的区域空气污染已成为紧迫、突出的环境问题。据相关研究表明，PM2.5在不同季节质量浓度差异较大。根据广州市2015~2019年的PM2.5月均质量浓度数据，结合大气污染物及气象因素，引入季节指数，建立预测PM2.5质量浓度的改进多元线性回归和多层感知器组合预测模型，探析广州市大气污染物中PM2.5质量浓度的变化规律。结果表明，用季节指数改进的组合预测模型对PM2.5质量浓度进行预测分析，拟合结果良好。使用不同评价指标将组合模型与传统的多层感知器预测模型和多元线性回归模型进行对比，该组合模型的均方根误差(Root Mean Square Error，RMSE)、平均绝对百分比误差(Mean Absolute Percentage Error，MAPE)、平均绝对误差(Mean Absolute Error，MAE)分别比多层感知器模型减少了23.1%、31%、24.2%；比多元线性回归模型减少了35.3%、41.3%、41%。该模型精度均优于传统的多元线性回归模型和多层感知器模型，能更好地预测环境PM2.5质量浓度，为优化环境提供参考。
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| 关 键 词： | PM2.5质量浓度预测 多元线性回归 多层感知器 神经网络 |
| 收稿时间： | 2021-06-03 |
PM2.5 Concentration Improving Prediction Modeling of Seasonal Index |
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| Zeng Jiang-yi,Li Zhi-sheng,Ou Yao-chun,Jin Yu-kai.PM2.5 Concentration Improving Prediction Modeling of Seasonal Index[J].Journal of Guangdong University of Technology,2022,39(3):89-94. |
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| Authors: | Zeng Jiang-yi Li Zhi-sheng Ou Yao-chun Jin Yu-kai |
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| Affiliation: | School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China |
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| Abstract: | In recent years, China's economy and urbanization have developed rapidly, and the development of cities often comes at the expense of the environment. Regional air pollution dominated by PM2.5 has become the most pressing and prominent environmental problem in China. According to relevant studies, PM2.5 concentrations vary greatly in different seasons. Based on the PM2.5 monthly mean concentration data of Guangzhou from 2015 to 2019, combined with atmospheric pollutants and meteorological factors, with the seasonal index introduced, an improved multiple linear regression and multi-layer perceptual combination prediction model for PM2.5 concentration is established, to analyze the variation law and future development trend of PM2.5 concentration in Guangzhou. The results show that the combined prediction model with the improved seasonal index is used to predict and analyze PM2.5, and the fitting results are good. Compared with the multi-layer perceptron prediction model by using different evaluation indexes, the RMSE(Root Mean Square Error), MAPE(Mean Absolute Percentage Error) and MAE(Mean Absolute Error) of the combined model are reduced by 23.1%, 31% and 24.2% . Compared with the multiple linear regression model, the reduction is 35.3%, 41.3% and 41% . The accuracy of the model is better than the traditional multiple linear regression model and multi-layer perceptron model, which can better predict environmental PM2.5 concentration and provide reference for optimizing the environment. |
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| Keywords: | PM2 5 concentration forecasting multiple linear regression multilayer perceptron neural networks |
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