Precipitation Nowcasting: Leveraging bidirectional LSTM and 1D CNN
This work addresses precipitation nowcasting for applications like flight safety and agriculture, but it is incremental as it applies existing deep learning methods to this domain.
The paper tackles short-term rainfall forecasting by applying a bidirectional LSTM and comparing it with a 1D CNN to demonstrate the superiority of sequence models over feed-forward architectures for this time series problem.
Short-term rainfall forecasting, also known as precipitation nowcasting has become a potentially fundamental technology impacting significant real-world applications ranging from flight safety, rainstorm alerts to farm irrigation timings. Since weather forecasting involves identifying the underlying structure in a huge amount of data, deep-learning based precipitation nowcasting has intuitively outperformed the traditional linear extrapolation methods. Our research work intends to utilize the recent advances in deep learning to nowcasting, a multi-variable time series forecasting problem. Specifically, we leverage a bidirectional LSTM (Long Short-Term Memory) neural network architecture which remarkably captures the temporal features and long-term dependencies from historical data. To further our studies, we compare the bidirectional LSTM network with 1D CNN model to prove the capabilities of sequence models over feed-forward neural architectures in forecasting related problems.