Tiny-RainNet: a deep convolutional neural network with bi-directional long short-term memory model for short-term rainfall prediction

Rainfall is not only related to current and previous meteorological conditions but also to meteorological conditions of the current location and surrounding regions. Existing short-term rainfall prediction methods mainly focus on radar echo extrapolation to predict the future radar echo maps, and then retrieve rainfall based on the predicted radar echo maps. These methods obtain rainfall through two separate steps usually leading to large accumulated errors. However, Tiny-RainNet is proposed by combining convolutional neural networks (CNNs) with bi-directional long short-term memory (BiLSTM) to directly predict future rainfall based on sequential radar echo maps. The structure of Tiny-RainNet is simpler than existing rainfall prediction models combining CNNs with LSTM. In order to further reduce computational complexity of the Tiny-RainNet and obtain good rainfall prediction results, 10 x 10, not the original 101 x 101, sequential radar maps are used as inputs of the Tiny-RainNet after making many tests considering temporal-spatial meteorological conditions. The proposed model takes into account the influence of temporal-spatial meteorological conditions on rainfall prediction. This avoids the accumulated error caused by multi-step prediction methods. The overall performance of the Tiny-RainNet model performs better than fully connected LSTM, LSTM and convolutional LSTM.