Short-term spatio-temporal forecasting of air temperatures using deep graph convolutional neural networks

Time series forecasting of meteorological variables, such as the hourly air temperature, has multiple benefits for industry, agriculture, and the environment. Due to the high accuracy required for the associated short-term predictions, traditional methods cannot satisfy the requirements and generall...

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Bibliographic Details
Published in:Stochastic environmental research and risk assessment 2023-05, Vol.37 (5), p.1649-1667
Main Authors: García-Duarte, Lucia, Cifuentes, Jenny, Marulanda, Geovanny
Format: Article
Language:English
Subjects:
Air temperature
Aquatic Pollution
Artificial neural networks
Chemistry and Earth Sciences
Climate change
Computational Intelligence
Computer Science
Deep learning
Earth and Environmental Science
Earth Sciences
Environment
Environmental research
Forecasting
Graph neural networks
Long short-term memory
Machine learning
Math. Appl. in Environmental Science
Mathematical models
Meteorological data
Neural networks
Original Paper
Physics
Probability Theory and Stochastic Processes
Radiation
Risk assessment
Solar energy
Spatial dependencies
Statistics for Engineering
Support vector machines
Temperature
Time series
Waste Water Technology
Water Management
Water Pollution Control
Weather
Weather forecasting
Weather stations
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Summary:Time series forecasting of meteorological variables, such as the hourly air temperature, has multiple benefits for industry, agriculture, and the environment. Due to the high accuracy required for the associated short-term predictions, traditional methods cannot satisfy the requirements and generally ignore spatial dependencies. This paper proposes a deep Graph Convolutional Long Short Term Memory Neural Network (GCN-LSTM) technique to tackle the time series prediction problem in air temperature forecasting. In the proposed methodology, temporal and spatial-based imputation approaches have been employed to recover the weather variables missing values. The proposed approach is validated using real, open weather data from 37 meteorological stations in Spain. Performed analysis indicates that GCN-LSTM showed superior performance when compared with various state-of-the-art Deep Learning based models found in the literature, resulting in a more robust and computationally efficient model for forecasting air temperature in many meteorological stations simultaneously.
ISSN:1436-3240
1436-3259
DOI:10.1007/s00477-022-02358-0