Atmospheric Temperature Prediction Based on a BiLSTM-Attention Model

To address the problem that traditional models are not effective in predicting atmospheric temperature, this paper proposes an atmospheric temperature prediction model based on symmetric BiLSTM (bidirectional long short-term memory)-Attention model. Firstly, the meteorological data from five major s...

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Main Authors: Xueli Hao, Ying Liu, Lili Pei, Wei Li, Yaohui Du
Format: Article
Language:English
Published: MDPI AG 2022-11-01
Series:Symmetry
Subjects:
Online Access:https://www.mdpi.com/2073-8994/14/11/2470
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author Xueli Hao
Ying Liu
Lili Pei
Wei Li
Yaohui Du
author_facet Xueli Hao
Ying Liu
Lili Pei
Wei Li
Yaohui Du
author_sort Xueli Hao
collection DOAJ
description To address the problem that traditional models are not effective in predicting atmospheric temperature, this paper proposes an atmospheric temperature prediction model based on symmetric BiLSTM (bidirectional long short-term memory)-Attention model. Firstly, the meteorological data from five major stations in Beijing were integrated, cleaned, and normalized to build an atmospheric temperature prediction dataset containing multiple feature dimensions; then, a BiLSTM memory network was used to construct with forward and backward information in the time dimension. And the limitations of the traditional LSTM method in long-term time series analysis were solved by introducing the attention mechanism to achieve the prediction analysis of atmospheric temperature. Finally, by comparing the prediction results with those of BiLSTM, LSTM-Attention, and LSTM, it is revealed that the proposed model has the best prediction effect, with a MAE value of 0.013, which is 0.72%, 0.41%, and 1.24% lower than those of BiLSTM, LSTM-Attention, and LSTM, respectively; the R<sup>2</sup> value reaches 0.9618, which is 2.73%, 1.23%, and 4.98% higher than BiLSTM, LSTM-Attention, and LSTM, respectively. The results show that the symmetrical BiLSTM-Attention atmospheric temperature prediction model can effectively improve the prediction accuracy of temperature data, and the model can also be used to predict other time series data.
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spelling doaj.art-cd0002cacc32493382a881ffcfff76d62023-11-24T10:14:35ZengMDPI AGSymmetry2073-89942022-11-011411247010.3390/sym14112470Atmospheric Temperature Prediction Based on a BiLSTM-Attention ModelXueli Hao0Ying Liu1Lili Pei2Wei Li3Yaohui Du4School of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaTo address the problem that traditional models are not effective in predicting atmospheric temperature, this paper proposes an atmospheric temperature prediction model based on symmetric BiLSTM (bidirectional long short-term memory)-Attention model. Firstly, the meteorological data from five major stations in Beijing were integrated, cleaned, and normalized to build an atmospheric temperature prediction dataset containing multiple feature dimensions; then, a BiLSTM memory network was used to construct with forward and backward information in the time dimension. And the limitations of the traditional LSTM method in long-term time series analysis were solved by introducing the attention mechanism to achieve the prediction analysis of atmospheric temperature. Finally, by comparing the prediction results with those of BiLSTM, LSTM-Attention, and LSTM, it is revealed that the proposed model has the best prediction effect, with a MAE value of 0.013, which is 0.72%, 0.41%, and 1.24% lower than those of BiLSTM, LSTM-Attention, and LSTM, respectively; the R<sup>2</sup> value reaches 0.9618, which is 2.73%, 1.23%, and 4.98% higher than BiLSTM, LSTM-Attention, and LSTM, respectively. The results show that the symmetrical BiLSTM-Attention atmospheric temperature prediction model can effectively improve the prediction accuracy of temperature data, and the model can also be used to predict other time series data.https://www.mdpi.com/2073-8994/14/11/2470bidirectional long short-term memory networkattention mechanismmachine learningtemperature prediction
spellingShingle Xueli Hao
Ying Liu
Lili Pei
Wei Li
Yaohui Du
Atmospheric Temperature Prediction Based on a BiLSTM-Attention Model
Symmetry
bidirectional long short-term memory network
attention mechanism
machine learning
temperature prediction
title Atmospheric Temperature Prediction Based on a BiLSTM-Attention Model
title_full Atmospheric Temperature Prediction Based on a BiLSTM-Attention Model
title_fullStr Atmospheric Temperature Prediction Based on a BiLSTM-Attention Model
title_full_unstemmed Atmospheric Temperature Prediction Based on a BiLSTM-Attention Model
title_short Atmospheric Temperature Prediction Based on a BiLSTM-Attention Model
title_sort atmospheric temperature prediction based on a bilstm attention model
topic bidirectional long short-term memory network
attention mechanism
machine learning
temperature prediction
url https://www.mdpi.com/2073-8994/14/11/2470
work_keys_str_mv AT xuelihao atmospherictemperaturepredictionbasedonabilstmattentionmodel
AT yingliu atmospherictemperaturepredictionbasedonabilstmattentionmodel
AT lilipei atmospherictemperaturepredictionbasedonabilstmattentionmodel
AT weili atmospherictemperaturepredictionbasedonabilstmattentionmodel
AT yaohuidu atmospherictemperaturepredictionbasedonabilstmattentionmodel