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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MDPI AG
2022-11-01
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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. |
first_indexed | 2024-03-09T17:57:30Z |
format | Article |
id | doaj.art-cd0002cacc32493382a881ffcfff76d6 |
institution | Directory Open Access Journal |
issn | 2073-8994 |
language | English |
last_indexed | 2024-03-09T17:57:30Z |
publishDate | 2022-11-01 |
publisher | MDPI AG |
record_format | Article |
series | Symmetry |
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 |