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Urban land surface temperature prediction using parallel STL-Bi-LSTM neural network

文献类型：期刊论文

作者Huo, Xing2; Cui, Guangpeng2; Ma, Lingling3; Tang, Bohui1; Tang, Ronglin4; Shao, Kun2; Wang, Xinhong3
刊名JOURNAL OF APPLIED REMOTE SENSING
出版日期2022-07-01
卷号16期号:3页码:13
关键词temperature prediction time series seasonal and trend decomposition using loess long short-term memory
DOI10.1117/1.JRS.16.034529
通讯作者Ma, Lingling(mall@aircas.ac.cn)
英文摘要Accurate temperature prediction is of great significance to human life and social economy. A series of traditional methods and machine learning methods have been proposed to achieve temperature prediction, but it is still a challenging problem. We propose a temperature prediction model that combines seasonal and trend decomposition using loess (STL) and the bidirectional long short-term memory (Bi-LSTM) network to achieve high-accuracy prediction of the daily average temperature of China cities. The proposed model decomposes the temperature data using STL into trend component, seasonal component, and remainder component. Decomposition components and the original temperature data are input into the two-layer Bi-LSTM to learn the features of the temperature data, and the sum of prediction of three components and the original temperature data prediction result are added using learnable weights as the prediction result. The experimental results show that the average root mean square error and mean absolute error of the proposed model on the testing data are 0.11 and 0.09, respectively, which are lower than 0.35 and 0.27 of STL-LSTM, 2.73 and 2.07 of EMD-LSTM, 0.39 and 0.15 of STL-SVM, achieving a higher precision temperature prediction. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License.
WOS关键词IMPACT ; HEAT
资助项目National Natural Science Foundation of China[61872407]
WOS研究方向Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000867557000031
出版者SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
资助机构National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/186402]  
专题中国科学院地理科学与资源研究所
通讯作者Ma, Lingling
作者单位1.Kunming Univ Sci & Technol, Kunming, Yunnan, Peoples R China
2.Hefei Univ Technol, Hefei, Peoples R China
3.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Quantitat Remote Sensing Informat Technol, Beijing, Peoples R China
4.Chinese Acad Sc, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Huo, Xing,Cui, Guangpeng,Ma, Lingling,et al. Urban land surface temperature prediction using parallel STL-Bi-LSTM neural network[J]. JOURNAL OF APPLIED REMOTE SENSING,2022,16(3):13.
APA Huo, Xing.,Cui, Guangpeng.,Ma, Lingling.,Tang, Bohui.,Tang, Ronglin.,...&Wang, Xinhong.(2022).Urban land surface temperature prediction using parallel STL-Bi-LSTM neural network.JOURNAL OF APPLIED REMOTE SENSING,16(3),13.
MLA Huo, Xing,et al."Urban land surface temperature prediction using parallel STL-Bi-LSTM neural network".JOURNAL OF APPLIED REMOTE SENSING 16.3(2022):13.

入库方式： OAI收割

来源：地理科学与资源研究所

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