Prediction of Hourly PM2.5 and PM10 Concentrations in Chongqing City in China Based on Artificial Neural Network
文献类型：期刊论文
| 作者 | Guo, Qingchun2,3,4; He, Zhenfang3,4,5; Wang, Zhaosheng1 |
| 刊名 | AEROSOL AND AIR QUALITY RESEARCH
 |
| 出版日期 | 2023-06-01 |
| 卷号 | 23期号:6页码:11 |
| 关键词 | Air pollution Artificial neural network Meteorological element Predict PM 2 5 PM 10 Chongqing Training function |
| ISSN号 | 1680-8584 |
| DOI | 10.4209/aaqr.220448 |
| 通讯作者 | Guo, Qingchun(guoqingchun@lcu.edu.cn) ; He, Zhenfang(hezhenfang@lcu.edu.cn) |
| 英文摘要 | Accurate prediction of air pollution is a difficult problem to be solved in atmospheric environment research. An Artificial Neural Network (ANN) is exploited to predict hourly PM2.5 and PM10 concentrations in Chongqing City. We take PM2.5 (PM10), time and meteorological elements as the input of the ANN, and the PM2.5 (PM10) of the next hour as the output to build an ANN model. Thirteen kinds of training functions are compared to obtain the optimal function. The research results display that the ANN model exhibits good performance in predicting hourly PM2.5 and PM10 concentrations. Trainbr is the best function for predicting PM2.5 concentrations compared to other training functions with R value (0.9783), RMSE (1.2271), and MAE (0.9050). Trainlm is the second best with R value (0.9495), RMSE (1.8845), and MAE (1.3902). Similarly, trainbr is also the best in forecasting PM10 concentrations with R value (0.9773), RMSE value (1.8270), and MAE value (1.4341). Trainlm is the second best with R value (0.9522), RMSE (2.6708), and MAE (1.8554). These two training functions have good generalization ability and can meet the needs of hourly PM2.5 and PM10 prediction. The forecast results can support fine management and help improve the ability to prevent and control air pollution in advance, accurately and scientifically. |
| WOS关键词 | POLLUTION |
| 资助项目 | National Natural Science Fund of China[41572150] ; Shandong Province Higher Educational Humanities and Social Science Fund[J18RA196] ; State Key Laboratory of Loess and Quaternary Geology Found[SKLLQG2211] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000993221900001 |
| 出版者 | TAIWAN ASSOC AEROSOL RES-TAAR |
| 资助机构 | National Natural Science Fund of China ; Shandong Province Higher Educational Humanities and Social Science Fund ; State Key Laboratory of Loess and Quaternary Geology Found |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/197367]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Guo, Qingchun; He, Zhenfang |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Natl Ecosyst Sci Data Ctr, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Inst Earth Environm, State Key Lab Loess & Quaternary Geol, Xian 710061, Peoples R China 3.Liaocheng Univ, Sch Geog & Environm, Liaocheng 252000, Peoples R China 4.Liaocheng Univ, Inst Huanghe Studies, Liaocheng 252000, Peoples R China 5.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guo, Qingchun,He, Zhenfang,Wang, Zhaosheng. Prediction of Hourly PM2.5 and PM10 Concentrations in Chongqing City in China Based on Artificial Neural Network[J]. AEROSOL AND AIR QUALITY RESEARCH,2023,23(6):11. |
| APA | Guo, Qingchun,He, Zhenfang,&Wang, Zhaosheng.(2023).Prediction of Hourly PM2.5 and PM10 Concentrations in Chongqing City in China Based on Artificial Neural Network.AEROSOL AND AIR QUALITY RESEARCH,23(6),11. |
| MLA | Guo, Qingchun,et al."Prediction of Hourly PM2.5 and PM10 Concentrations in Chongqing City in China Based on Artificial Neural Network".AEROSOL AND AIR QUALITY RESEARCH 23.6(2023):11. |
入库方式： OAI收割
来源：地理科学与资源研究所
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