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Status: Unchecked ×Keyword: rainfall intensity prediction ×Clear all

1 claim from 1 paper

1. Environmental Science › Hydrological Forecasting Using AI

   Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

   Shi, Chen, Wang, Yeung, Wong and Woo · arXiv (Cornell University) · 2015

   The authors frame precipitation nowcasting as spatiotemporal sequence forecasting and propose ConvLSTM, a model that adds convolutions to LSTM, reporting that it beats FC-LSTM and ROVER.

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   1. UncheckedThe paper reports that its ConvLSTM network predicts short-term rainfall better than FC-LSTM and the operational ROVER algorithm in its experiments.“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.”