Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models
文献类型：期刊论文
| 作者 | Guo, Qingchun1,3; He, Zhenfang4; Wang, Zhaosheng5 |
| 刊名 | FRONTIERS IN FORESTS AND GLOBAL CHANGE
 |
| 出版日期 | 2023-12-08 |
| 卷号 | 6页码:1249300 |
| 关键词 | extreme atmospheric temperature artificial neural network deep learning CNN-GRU CNN-LSTM prediction training algorithm forest |
| DOI | 10.3389/ffgc.2023.1249300 |
| 文献子类 | Article |
| 英文摘要 | IntroductionAtmospheric temperature affects the growth and development of plants and has an important impact on the sustainable development of forest ecological systems. Predicting atmospheric temperature is crucial for forest management planning.MethodsArtificial neural network (ANN) and deep learning models such as gate recurrent unit (GRU), long short-term memory (LSTM), convolutional neural network (CNN), CNN-GRU, and CNN-LSTM, were utilized to predict the change of monthly average and extreme atmospheric temperatures in Zhengzhou City. Average and extreme atmospheric temperature data from 1951 to 2022 were divided into training data sets (1951-2000) and prediction data sets (2001-2022), and 22 months of data were used as the model input to predict the average and extreme temperatures in the next month.Results and DiscussionThe number of neurons in the hidden layer was 14. Six different learning algorithms, along with 13 various learning functions, were trained and compared. The ANN model and deep learning models were evaluated in terms of correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), and good results were obtained. Bayesian regularization (trainbr) in the ANN model was the best performing algorithm in predicting average, minimum and maximum atmospheric temperatures compared to other algorithms in terms of R (0.9952, 0.9899, and 0.9721), and showed the lowest error values for RMSE (0.9432, 1.4034, and 2.0505), and MAE (0.7204, 1.0787, and 1.6224). The CNN-LSTM model showed the best performance. This CNN-LSTM method had good generalization ability and could be used to forecast average and extreme atmospheric temperature in other areas. Future climate changes were projected using the CNN-LSTM model. The average atmospheric temperature, minimum atmospheric temperature, and maximum atmospheric temperature in 2030 were predicted to be 17.23 degrees C, -5.06 degrees C, and 42.44 degrees C, whereas those in 2040 were predicted to be 17.36 degrees C, -3.74 degrees C, and 42.68 degrees C, respectively. These results suggest that the climate is projected to continue warming in the future. |
| WOS关键词 | SURFACE AIR-TEMPERATURE ; CNN ; PRECIPITATION ; CHINA |
| WOS研究方向 | Environmental Sciences & Ecology ; Forestry |
| WOS记录号 | WOS:001128513100001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200925]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Liaocheng Univ, Sch Geog & Environm, Liaocheng, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Natl Ecosyst Sci Data Ctr, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China 3.China Meteorol Adm, Key Lab Atmospher Chem, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Earth Environm, State Key Lab Loess & Quaternary Geol, Xian, Peoples R China 5.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guo, Qingchun,He, Zhenfang,Wang, Zhaosheng. Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models[J]. FRONTIERS IN FORESTS AND GLOBAL CHANGE,2023,6:1249300. |
| APA | Guo, Qingchun,He, Zhenfang,&Wang, Zhaosheng.(2023).Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models.FRONTIERS IN FORESTS AND GLOBAL CHANGE,6,1249300. |
| MLA | Guo, Qingchun,et al."Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models".FRONTIERS IN FORESTS AND GLOBAL CHANGE 6(2023):1249300. |
入库方式： OAI收割
来源：地理科学与资源研究所
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