基于深度学习模型的广州市大气PM<sub>2.5</sub>和PM<sub>10</sub>浓度预测

黄春桃; 范东平; 卢集富; 廖启丰

doi:10.13205/j.hjgc.202112020

基于深度学习模型的广州市大气PM_2.5和PM₁₀浓度预测

doi: 10.13205/j.hjgc.202112020

1. 广州华立学院, 广州 511300;
2. 生态环境部华南环境科学研究所, 广州 510530;
3. 广东科迪隆科技有限公司, 广州 510000

基金项目:

广东省重点学科培育项目(粤教研函[2017]1号)

广东省教育厅重点科研平台青年创新人才项目(自然科学)(2018KQNCX35)。

详细信息

通讯作者:
黄春桃(1985-),女,硕士,主要研究方向为大气污染治理、水处理技术。610140538@qq.com

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- 文章访问数: 625
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- 被引次数: 2
出版历程
- 收稿日期: 2021-05-12
- 网络出版日期: 2022-03-30
- 刊出日期: 2022-03-30

PREDICTION OF PM_2.5 AND PM₁₀ CONCENTRATION IN GUANGZHOU BASED ON DEEP LEARNING MODEL

1. Guangzhou Huali College, Guangzhou 511300, China;
2. South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510530, China;
3. Guangdong Kedilong Technology Co., Ltd, Guangzhou 510000, China

摘要

摘要: 精准预测大气污染颗粒物PM_2.5、PM₁₀浓度能为大气污染防治提供科学依据,但目前较多PM_2.5和PM₁₀浓度预测在缺少污染源排放清单和能见度数据时,预测精度不高。而目前深度学习模型应用于PM_2.5和PM₁₀浓度预测的研究还鲜见报道。基于广州市2015年6月1日—2018年1月10日的空气质量和气象监测历史数据,分别构建了随机森林模型(RF)、XGBoost模型2种传统的机器学习模型和长短时记忆网络(LSTM)、门控循环单元网络(GRU)2种深度学习模型,并对广州市的PM_2.5、PM₁₀日均浓度值进行预测。结果表明:在缺少污染源排放清单和能见度数据时,4种模型也能较好地预测PM_2.5、PM₁₀日均浓度。根据MSE、RMSE、MAPE、MAE和R²等评价指标,对4个模型的PM_2.5、PM₁₀预测效果进行测评,得出深度学习GRU模型预测效果均为最佳,RF模型的预测结果均为最差。相比目前研究及应用较多的RF模型、XGBoost模型、LSTM模型,基于深度学习的GRU模型能更好地预测PM_2.5、PM₁₀浓度。
- PM_2.5 /
- PM₁₀ /
- 深度学习模型 /
- 浓度预测 /
- 影响因素
Abstract: Precisely predicting the concentration of PM_2.5 and PM₁₀ in air pollution can provide a scientific basis for the prevention and control of air pollution. However, in the absence of pollution source emission inventory and visibility data, the prediction accuracy of the existing PM_2.5 and PM₁₀ concentration prediction methods are not high. In addition, it is rarely reported that the current deep learning models have been applied successively to PM_2.5 and PM₁₀ concentration prediction research. Based on the historical air quality monitoring data and weather monitoring historical data in Guangzhou from June 1, 2015 to January 10, 2018, two traditional machine learning models(random forest model(RF) and XGBoost model) and two deep learning models(short-long-term memory network(LSTM) and gated recurrent unit network(GRU) were constructed respectively, to predict the daily average concentration of PM_2.5 and PM₁₀ in Guangzhou. The results showed that the four models could also well predict the daily average concentration of PM_2.5 and PM₁₀ in the absence of pollution source emission inventory and visibility data. According to the evaluation metrics, i.e., MSE, RMSE, MAPE, MAE, and R², the PM_2.5 and PM₁₀ prediction effects of the four models were evaluated. The results indicated that the prediction effect of the deep GRU model was the best and the prediction results of the RF model were the worst. Compared with the commonly used RF model, XGBoost model, and LSTM model, the GRU model based on deep learning could better predict PM_2.5 and PM₁₀ concentration.
- PM_2.5 /
- PM₁₀ /
- deep learning /
- concentration prediction /
- influencing factors

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