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A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models

文献类型：期刊论文

作者Guo, Qingchun1,3,4,5; He, Zhenfang1,5; Wang, Zhaosheng2; Qiao, Shuaisen5; Zhu, Jingshu5; Chen, Jiaxin5
刊名WATER
出版日期2024-10-01
卷号16期号:19页码:20
关键词artificial intelligence neural network gate recurrent unit long short-term memory convolutional neural network average atmospheric temperature precipitation
DOI10.3390/w16192870
产权排序5
英文摘要Climate change affects the water cycle, water resource management, and sustainable socio-economic development. In order to accurately predict climate change in Weifang City, China, this study utilizes multiple data-driven deep learning models. The climate data for 73 years include monthly average air temperature (MAAT), monthly average minimum air temperature (MAMINAT), monthly average maximum air temperature (MAMAXAT), and monthly total precipitation (MP). The different deep learning models include artificial neural network (ANN), recurrent NN (RNN), gate recurrent unit (GRU), long short-term memory neural network (LSTM), deep convolutional NN (CNN), hybrid CNN-GRU, hybrid CNN-LSTM, and hybrid CNN-LSTM-GRU. The CNN-LSTM-GRU for MAAT prediction is the best-performing model compared to other deep learning models with the highest correlation coefficient (R = 0.9879) and lowest root mean square error (RMSE = 1.5347) and mean absolute error (MAE = 1.1830). These results indicate that The hybrid CNN-LSTM-GRU method is a suitable climate prediction model. This deep learning method can also be used for surface water modeling. Climate prediction will help with flood control and water resource management.
WOS关键词NEURAL-NETWORKS
资助项目Shandong Provincial Natural Science Foundation ; State Key Laboratory of Loess and Quaternary Geology Foundation[SKLLQG2211] ; Shandong Province Higher Educational Humanities and Social Science Program[J18RA196] ; National Natural Science Foundation of China[41572150] ; Junior Faculty Support Program for Scientific and Technological Innovations in Shandong Provincial Higher Education Institutions[2021KJ085] ; [ZR2023MD075] ; [2023B02]
WOS研究方向Environmental Sciences & Ecology ; Water Resources
语种英语
WOS记录号WOS:001332071500001
出版者MDPI
资助机构Shandong Provincial Natural Science Foundation ; State Key Laboratory of Loess and Quaternary Geology Foundation ; Shandong Province Higher Educational Humanities and Social Science Program ; National Natural Science Foundation of China ; Junior Faculty Support Program for Scientific and Technological Innovations in Shandong Provincial Higher Education Institutions
源URL[http://ir.igsnrr.ac.cn/handle/311030/209872]  
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.Liaocheng Univ, Inst Huanghe Studies, Liaocheng 252000, Peoples R China
2.Chinese Acad Sci, Natl Ecosyst Sci Data Ctr, Key Lab Ecosyst Network Observat & Modeling, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
3.Chinese Acad Sci, Inst Earth Environm, State Key Lab Loess & Quaternary Geol, Xian 710061, Peoples R China
4.China Meteorol Adm, Key Lab Atmospher Chem, Beijing 100081, Peoples R China
5.Liaocheng Univ, Sch Geog & Environm, Liaocheng 252000, Shandong, Peoples R China
推荐引用方式
GB/T 7714		 Guo, Qingchun,He, Zhenfang,Wang, Zhaosheng,et al. A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models[J]. WATER,2024,16(19):20.
APA Guo, Qingchun,He, Zhenfang,Wang, Zhaosheng,Qiao, Shuaisen,Zhu, Jingshu,&Chen, Jiaxin.(2024).A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models.WATER,16(19),20.
MLA Guo, Qingchun,et al."A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models".WATER 16.19(2024):20.

入库方式： OAI收割

来源：地理科学与资源研究所

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