Prediction of hourly air temperature based on CNN-LSTM

The prediction accuracy of hourly air temperature is generally poor because of random changes, long time series, and the nonlinear relationship between temperature and other meteorological elements, such as air pressure, dew point, and wind speed. In this study, two deep-learning methods-a convoluti...

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Bibliographic Details
Published in:Geomatics, natural hazards and risk natural hazards and risk, 2022-12, Vol.13 (1), p.1962-1986
Main Authors: Hou, Jingwei, Wang, Yanjuan, Zhou, Ji, Tian, Qiong
Format: Article
Language:English
Subjects:
Air temperature
Artificial neural networks
Cloud amount
CNN-LSTM
convolutional neural network
deep learning
Dew
Dew point
Dew point method
Goodness of fit
Hourly
Hourly temperature prediction
Long short-term memory
Long term memory
Machine learning
Meteorological data
Modelling
Neural networks
Predictions
Temperature
Temperature data
Time series
Training
Weather stations
Wind
Wind direction
Wind speed
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Summary:The prediction accuracy of hourly air temperature is generally poor because of random changes, long time series, and the nonlinear relationship between temperature and other meteorological elements, such as air pressure, dew point, and wind speed. In this study, two deep-learning methods-a convolutional neural network (CNN) and long short-term memory (LSTM)-are integrated into a network model (CNN-LSTM) for hourly temperature prediction. The CNN reduces the dimensionality of the time-series data, while LSTM captures the long-term memory of the massive temperature time-series data. Training and validation sets are constructed using 60,133 hourly meteorological data (air temperature, dew point, air pressure, wind direction, wind speed, and cloud amount) obtained from January 2000 to October 2020 at the Yinchuan meteorological station in China. Mean absolute error (MAE), mean absolute percentage error (MAPE), and goodness of fit are used to compare the performances of the CNN, LSTM, and CNN-LSTM models. The results show that MAE, MAPE, RMSE, and PBIAS from the CNN-LSTM model for hourly temperature prediction are 0.82, 0.63, 2.05, and 2.18 in the training stage and 1.02, 0.8, 1.97, and −0.08 in the testing stage. Average goodness of fit from the CNN-LSTM model is 0.7258, higher than the CNN (0.5291), and LSTM (0.5949) models. The hourly temperatures predicted by the CNN-LSTM model are highly consistent with the measured values, especially for long time series of hourly temperature data.
ISSN:1947-5705
1947-5713
DOI:10.1080/19475705.2022.2102942