Article

Convolutional LSTM Network: a machine learning approach for precipitation nowcasting

Authors:

Wang-chun WooAuthors Info & Claims

NIPS'15: Proceedings of the 29th International Conference on Neural Information Processing Systems - Volume 1

Pages 802 - 810

Published: 07 December 2015 Publication History

Abstract

The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine learning perspective. In this paper, we formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem in which both the input and the prediction target are spatiotemporal sequences. By extending the fully connected LSTM (FC-LSTM) to have convolutional structures in both the input-to-state and state-to-state transitions, we propose the convolutional LSTM (ConvLSTM) and use it to build an end-to-end trainable model for the precipitation nowcasting problem. Experiments show that our ConvLSTM network captures spatiotemporal correlations better and consistently outperforms FC-LSTM and the state-of-the-art operational ROVER algorithm for precipitation nowcasting.

References

[1]

F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. Goodfellow, A. Bergeron, N. Bouchard, D. Warde-Farley, and Y. Bengio. Theano: New features and speed improvements. Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop, 2012.

[2]

Y. Bengio, I. Goodfellow, and A. Courville. Deep Learning. Book in preparation for MIT Press, 2015.

[3]

J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, and Y. Bengio. Theano: a CPU and GPU math expression compiler. In Scipy, volume 4, page 3. Austin, TX, 2010.

[4]

R. Bridson. Fluid Simulation for Computer Graphics. Ak Peters Series. Taylor & Francis, 2008.

Digital Library

[5]

T. Brox, A. Bruhn, N. Papenberg, and J. Weickert. High accuracy optical flow estimation based on a theory for warping. In ECCV, pages 25-36. 2004.

[6]

P. Cheung and H.Y. Yeung. Application of optical-flow technique to significant convection nowcast for terminal areas in Hong Kong. In the 3rd WMO International Symposium on Nowcasting and Very Short-Range Forecasting (WSN12), pages 6-10, 2012.

[7]

K. Cho, B. van Merrienboer, C. Gulcehre, F. Bougares, H. Schwenk, and Y. Bengio. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In EMNLP, pages 1724-1734, 2014.

[8]

J. Donahue, L. A. Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell. Long-term recurrent convolutional networks for visual recognition and description. In CVPR, 2015.

[9]

R. H. Douglas. The stormy weather group (Canada). In Radar in Meteorology, pages 61-68. 1990.

[10]

Urs Germann and Isztar Zawadzki. Scale-dependence of the predictability of precipitation from continental radar images. Part I: Description of the methodology. Monthly Weather Review, 130(12):2859-2873, 2002.

[11]

A. Graves. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850, 2013.

[12]

S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Computation, 9(8):1735-1780, 1997.

Digital Library

[13]

A. Karpathy and L. Fei-Fei. Deep visual-semantic alignments for generating image descriptions. In CVPR, 2015.

[14]

B. Klein, L. Wolf, and Y. Afek. A dynamic convolutional layer for short range weather prediction. In CVPR, 2015.

[15]

P.W. Li, W.K. Wong, K.Y. Chan, and E. S.T. Lai. SWIRLS-An Evolving Nowcasting System. Hong Kong Special Administrative Region Government, 2000.

[16]

J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015.

Digital Library

[17]

R. Pascanu, T. Mikolov, and Y. Bengio. On the difficulty of training recurrent neural networks. In ICML, pages 1310-1318, 2013.

Digital Library

[18]

M. Ranzato, A. Szlam, J. Bruna, M. Mathieu, R. Collobert, and S. Chopra. Video (language) modeling: a baseline for generative models of natural videos. arXiv preprint arXiv:1412.6604, 2014.

[19]

M. Reyniers. Quantitative Precipitation Forecasts Based on Radar Observations: Principles, Algorithms and Operational Systems. Institut Royal Météorologique de Belgique, 2008.

[20]

H. Sakaino. Spatio-temporal image pattern prediction method based on a physical model with time-varying optical flow. IEEE Transactions on Geoscience and Remote Sensing, 51(5-2):3023-3036, 2013.

[21]

N. Srivastava, E. Mansimov, and R. Salakhutdinov. Unsupervised learning of video representations using lstms. In ICML, 2015.

Digital Library

[22]

J. Sun, M. Xue, J. W. Wilson, I. Zawadzki, S. P. Ballard, J. Onvlee-Hooimeyer, P. Joe, D. M. Barker, P. W. Li, B. Golding, M. Xu, and J. Pinto. Use of NWP for nowcasting convective precipitation: Recent progress and challenges. Bulletin of the American Meteorological Society, 95(3):409-426, 2014.

[23]

I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. In NIPS, pages 3104-3112, 2014.

Digital Library

[24]

T. Tieleman and G. Hinton. Lecture 6.5 - RMSProp: Divide the gradient by a running average of its recent magnitude. Coursera Course: Neural Networks for Machine Learning, 4, 2012.

[25]

W.C. Woo and W.K. Wong. Application of optical flow techniques to rainfall nowcasting. In the 27th Conference on Severe Local Storms, 2014.

[26]

K. Xu, J. Ba, R. Kiros, A. Courville, R. Salakhutdinov, R. Zemel, and Y. Bengio. Show, attend and tell: Neural image caption generation with visual attention. In ICML, 2015.

Digital Library

Cited By

Ma TSu YAbdel Wahab MKhalil A(2025)ZPDSN: spatio-temporal meteorological forecasting with topological data analysisApplied Intelligence10.1007/s10489-024-06053-155:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s10489-024-06053-1
Xu GNg MYe YLi XSong GZhang BHuang Z(2024)TLS-MWP: A Tensor-Based Long- and Short-Range Convolution for Multiple Weather PredictionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.337929134:9(8382-8397)Online publication date: 20-Mar-2024
https://dl.acm.org/doi/10.1109/TCSVT.2024.3379291
Zhang HJia DMa H(2024)Rainy day image semantic segmentation based on two-stage progressive networkThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-024-03287-540:12(8945-8956)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s00371-024-03287-5
Show More Cited By

Index Terms

Convolutional LSTM Network: a machine learning approach for precipitation nowcasting

Index terms have been assigned to the content through auto-classification.

Recommendations

Two-stream convolutional LSTM for precipitation nowcasting
Abstract
Reliable precipitation nowcasting is essential to many fields, which can guide people to reasonably carry out production activities and respond to rainstorm disasters. However, precipitation nowcasting is a very challenging task because of ...
A spatio-temporal graph-guided convolutional LSTM for tropical cyclones precipitation nowcasting
Abstract
Real-time and accurate Tropical Cyclone (TC) precipitation nowcasting plays an important role in disaster prevention and mitigation. The strong ability of deep learning has been proved in many precipitation nowcasting studies. However, ...
Highlights
- We develop a novel deep-learning framework for TC precipitation nowcasting.
- We ...
A novel short receptive field based dilated causal convolutional network integrated with Bidirectional LSTM for short-term load forecasting
Highlights
- A novel hybrid Encoder-Decoder (ED) model is proposed for STLF problem.
- In ED model, the SRDCC and BiLSTM are cascaded to improve STLF accuracy.
- The proposed SRDCC-BiLSTM model mitigates overfitting issue of the STLF problem.
- A ...
Abstract
The Short-Term Load Forecasting (STLF) is a pre-eminent task for reliable power generation and electrical load dispatching in the power system. Numerous machine-learning and deep-learning forecasting algorithms have been presented in literature ...

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings

NIPS'15: Proceedings of the 29th International Conference on Neural Information Processing Systems - Volume 1

December 2015

3626 pages

Publisher

MIT Press

Cambridge, MA, United States

Publication History

Published: 07 December 2015

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

146
Total Citations
View Citations
3
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 09 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ma TSu YAbdel Wahab MKhalil A(2025)ZPDSN: spatio-temporal meteorological forecasting with topological data analysisApplied Intelligence10.1007/s10489-024-06053-155:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s10489-024-06053-1
Xu GNg MYe YLi XSong GZhang BHuang Z(2024)TLS-MWP: A Tensor-Based Long- and Short-Range Convolution for Multiple Weather PredictionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.337929134:9(8382-8397)Online publication date: 20-Mar-2024
https://dl.acm.org/doi/10.1109/TCSVT.2024.3379291
Zhang HJia DMa H(2024)Rainy day image semantic segmentation based on two-stage progressive networkThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-024-03287-540:12(8945-8956)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s00371-024-03287-5
Azmat MMadondo MBawa ADipietro KHoresh RJacobs MSrinivasan RO'Donncha FElkind E(2023)Forecasting soil moisture using domain inspired temporal graph convolution neural networks to guide sustainable crop managementProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/654(5897-5905)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/654
Zhong FBi XZhang YZhang WWang YWilliams BChen YNeville J(2023)RSPTProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i3.25482(3705-3714)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i3.25482
Cullen ARubinstein BKandeepan SFlower BLeong P(2023)Predicting dynamic spectrum allocation: a review covering simulation, modelling, and predictionArtificial Intelligence Review10.1007/s10462-023-10449-956:10(10921-10959)Online publication date: 3-Mar-2023
https://dl.acm.org/doi/10.1007/s10462-023-10449-9
Pan MZhu XWang YYang XKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Iso-dreamProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601954(23178-23191)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3601954
Bi XAdomavicius GLi WQu A(2022)Improving Sales Forecasting AccuracyINFORMS Journal on Computing10.1287/ijoc.2021.114734:3(1644-1660)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1287/ijoc.2021.1147
Taylor JLarraondo Pde Supinski B(2022)Data-driven global weather predictions at high resolutionsInternational Journal of High Performance Computing Applications10.1177/1094342021103981836:2(130-140)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1177/10943420211039818
Kumar ASatyanarayana Reddy SMahommad GKhan BSharma R(2022)Smart HealthcareScientific Programming10.1155/2022/20906812022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/2090681
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Table of Contents