Assessing the effectiveness of long short-term memory and artificial neural network in predicting daily ozone concentrations in Liaocheng City
文献类型：期刊论文
| 作者 | Guo, Qingchun2,3,4,5; He, Zhenfang4,5; Wang, Zhaosheng1 |
| 刊名 | SCIENTIFIC REPORTS
 |
| 出版日期 | 2025-02-25 |
| 卷号 | 15期号:1页码:6798 |
| 关键词 | Artificial neural network Long short-term memory Deep Learning Ozone |
| ISSN号 | 2045-2322 |
| DOI | 10.1038/s41598-025-91329-w |
| 产权排序 | 5 |
| 文献子类 | Article |
| 英文摘要 | Ozone pollution affects food production, human health, and the lives of individuals. Due to rapid industrialization and urbanization, Liaocheng has experienced increasing of ozone concentration over several years. Therefore, ozone has become a major environmental problem in Liaocheng City. Long short-term memory (LSTM) and artificial neural network (ANN) models are established to predict ozone concentrations in Liaocheng City from 2014 to 2023. The results show a general improvement in the accuracy of the LSTM model compared to the ANN model. Compared to the ANN, the LSTM has an increase in determination coefficient (R2), value from 0.6779 to 0.6939, a decrease in root mean square error (RMSE) value from 27.9895 mu g/m3 to 27.2140 mu g/m3 and a decrease in mean absolute error (MAE) value from 21.6919 mu g/m3 to 20.8825 mu g/m3. The prediction accuracy of the LSTM is superior to the ANN in terms of R, RMSE, and MAE. In summary, LSTM is a promising technique for predicting ozone concentrations. Moreover, by leveraging historical data and LSTM enables accurate predictions of future ozone concentrations on a global scale. This model will open up new avenues for controlling and mitigating ozone pollution. |
| URL标识 | 查看原文 |
| WOS关键词 | AIR-POLLUTION ; MODELS ; EXPOSURE |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:001433306200019 |
| 出版者 | NATURE PORTFOLIO |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/213316]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 通讯作者 | Guo, Qingchun |
| 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.China Meteorol Adm, Key Lab Atmospher Chem, Beijing 100081, Peoples R China; 3.Chinese Acad Sci, Inst Earth Environm, State Key Lab Loess & Quaternary Geol, Xian 710061, Peoples R China; 4.Liaocheng Univ, Inst Huanghe Studies, Liaocheng 252000, Peoples R China; 5.Liaocheng Univ, Sch Geog & Environm, Liaocheng 252000, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Guo, Qingchun,He, Zhenfang,Wang, Zhaosheng. Assessing the effectiveness of long short-term memory and artificial neural network in predicting daily ozone concentrations in Liaocheng City[J]. SCIENTIFIC REPORTS,2025,15(1):6798. |
| APA | Guo, Qingchun,He, Zhenfang,&Wang, Zhaosheng.(2025).Assessing the effectiveness of long short-term memory and artificial neural network in predicting daily ozone concentrations in Liaocheng City.SCIENTIFIC REPORTS,15(1),6798. |
| MLA | Guo, Qingchun,et al."Assessing the effectiveness of long short-term memory and artificial neural network in predicting daily ozone concentrations in Liaocheng City".SCIENTIFIC REPORTS 15.1(2025):6798. |
入库方式： OAI收割
来源：地理科学与资源研究所
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