Brief Review — Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting,
ConvLSTM, by Hong Kong University of Science and Technology, and Hong Kong Observatory
2015 NIPS, Over 5500 Citations (Sik-Ho Tsang @ Medium)
- ConvLSTM is proposed where convolution is added into LSTM for forecast prediction.
1.1. Standard FC-LSTM
- LSTM as a special RNN structure has proven stable and powerful for modeling long-range dependencies in various previous studies.
- FC-LSTM may be seen as a multivariate version of LSTM where the input, cell output and states are all 1D vectors:
- (Please visit LSTM if interested.)
1.2. Proposed ConvLSTM
- An extension of FC-LSTM, ConvLSTM, is proposed, which has convolutional structures in both the input-to-state and state-to-state transitions:
- where * is the convolutional operator.
- In this sense, FC-LSTM is actually a special case of ConvLSTM with all features standing on a single cell.
- Zero padding is used in the hidden state.
1.3. Encoding-Forecasting Structure
- For precipitation nowcasting, the observation at every timestamp is a 2D radar echo map.
- If we divide the map into tiled non-overlapping patches and view the pixels inside a patch as its measurements (see the above figure), the nowcasting problem naturally becomes a spatiotemporal sequence forecasting problem.
- For the proposed spatiotemporal sequence forecasting problem, the model consists of two networks, an encoding network and a forecasting network.
- The encoding LSTM compresses the whole input sequence into a hidden state tensor and the forecasting LSTM unfolds this hidden state to give the final prediction:
2.1. Moving-MNIST Dataset
- Two handwritten digits bouncing inside a 64×64 patch. The starting position and velocity direction are chosen uniformly at random and the velocity amplitude is chosen randomly in [3; 5).
ConvLSTM obtains lower loss than FC-LSTM.
The model can separate the overlapping digits successfully and predict the overall motion although the predicted digits are quite blurred.
2.2. Radar Echo Dataset
- The radar echo dataset used in this paper is a subset of the three-year weather radar intensities collected in Hong Kong from 2011 to 2013. Since not every day is rainy and our nowcasting target is precipitation, the top 97 rainy days are selected to form the dataset.
- The performance of the FC-LSTM network is not so good for this task, which is mainly caused by the strong spatial correlation in the radar maps, i.e., the motion of clouds is highly consistent in a local region.
ConvLSTM outperforms the optical flow based ROVER algorithm, since it is trained end-to-end for this task and some complex spatiotemporal patterns in the dataset can be learned by the nonlinear and convolutional structure of the network.
- Some prediction results of ROVER2 and ConvLSTM are shown above.
ConvLSTM gains high citation. It is amazing that it is partially done by HK Observatory! I always watch their weather forecast!