Abstract
Spatial-temporal graph neural networks (ST-GNN) have been shown to be highly effective for flow prediction in dynamic systems, but are under explored for weather prediction applications. We compare and evaluate Graph WaveNet (GWN) and the Low Rank Weighted Graph Neural Network (WGN) for weather prediction in South Africa. We compare these results to two basic temporal deep neural networks architectures, i.e. the Long Short-Term Memory (LSTM) and the Temporal Convolutional Neural Network (TCN), for maximum temperature prediction across 21 weather stations in South Africa. We also perform rigorous experiments to evaluate the stability and robustness of both ST-GNNs. The results show that the GWN model outperforms the other models across different prediction horizons with an average SMAPE score of 8.30%. We also analyse and compare learnt adjacency matrices of the two ST-GNNs to gain insights into the prominent spatial-temporal dependencies between weather stations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cao, D., et al.: Spectral temporal graph neural network for multivariate time-series forecasting (2021)
Cifuentes Quintero, J., Marulanda, G., Bello, A., Reneses, J.: Air temperature forecasting using machine learning techniques: a review. Energies 13, 4215 (2020). https://doi.org/10.3390/en13164215
Dhevi, A.: Imputing missing values using inverse distance weighted interpolation for time series data, pp. 255–259 (2014). https://doi.org/10.1109/ICoAC.2014.7229721
Hewage, P., Trovati, M., Pereira, E., et al.: Deep learning-based effective fine-grained weather forecasting model (2020)
Iseh, A.J., Woma, T.Y.: Weather forecasting models, methods and applications. Int. J. Eng. Res. Technol. 2 (2013)
Kouassi, K.H., Moodley, D.: An analysis of deep neural networks for predicting trends in time series data. In: SACAIR 2021. CCIS, vol. 1342, pp. 119–140. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66151-9_8
Pillay, K., Moodley, D.: Exploring graph neural networks for stock market prediction on the JSE. In: Jembere, E., Gerber, A.J., Viriri, S., Pillay, A. (eds.) SACAIR 2021. CCIS, vol. 1551, pp. 95–110. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-95070-5_7
Wilson, T., Tan, P.N., Luo, L.: A low rank weighted graph convolutional approach to weather prediction (2018). https://doi.org/10.1109/ICDM.2018.00078
Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., Zhang, C.: Connecting the dots: multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 753–763 (2020)
Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph WaveNet for deep spatial-temporal graph modeling (2019). https://doi.org/10.24963/ijcai.2019/264
Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (2018). http://dx.doi.org/10.24963/ijcai.2018/505
Zahroh, S., Hidayat, Y., Pontoh, R., Santoso, A., Bon, A., Firman, S.: Modeling and forecasting daily temperature in Bandung (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Davidson, M., Moodley, D. (2022). ST-GNNs for Weather Prediction in South Africa. In: Pillay, A., Jembere, E., Gerber, A. (eds) Artificial Intelligence Research. SACAIR 2022. Communications in Computer and Information Science, vol 1734. Springer, Cham. https://doi.org/10.1007/978-3-031-22321-1_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-22321-1_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-22320-4
Online ISBN: 978-3-031-22321-1
eBook Packages: Computer ScienceComputer Science (R0)