DeepRain: ConvLSTM Network for Precipitation Prediction using Multichannel Radar Data

X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-k. Wong, and W.-c. Woo, “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting,” in CVPR

2015

Earlier work this paper cites.

P. M. Sulagna Gope, Sudeshna Sarkar, “PREDICTION OF EXTREME RAINFALL USING HYBRID CONVOLUTIONAL-LONG SHORT TERM MEMORY NETWORKS,” Proceedings of the 6th International Workshop on Climate Informatics: CI 2016

2016

Earlier work this paper cites.

W. D. Yong Zhuang, “LONG-LEAD PREDICTION OF EXTREME PRECIPITATION CLUSTER VIA A SPATIO-TEMPORAL CONVOLUTIONAL NEURAL NETWORK,” in Proceedings of the 6th International Workshop on Climate Informatics: CI 2016, NCAR Technical Notes NCAR/TN-529+PROC

2016

Cited alongside, same era.

W. Zhang, L. Han, J. Sun, H. Guo, and J. Dai, “Application of Multi-channel 3D-cube Successive Convolution Network for Convective Storm Nowcasting,” in CVPR

2017

Cited alongside, same era.

“Korean National Weather Radar Center, http://radar.kma.go.kr/eng/radar/composition.do.”

Cited in the paper.

Shenzhen Meteorological Bureau-Alibab, “Short-Term Quantitative Precipitation Forecasting.”

Cited in the paper.

Seongchan Kim, Seungkyun Hong, and Sa-kwang Song, “DeepRain: A Predictive LSTM Network for Precipitation using Radar Data,” in Proceedings of the Korean Computer Congress(KCC 2017)

Original

2017

Closest in time.