  Accurate rainfall forecasting is critical because it has a great impact on
people's social and economic activities. Recent trends on various literatures
show that Deep Learning (Neural Network) is a promising methodology to tackle
many challenging tasks. In this study, we introduce a brand-new data-driven
precipitation prediction model called DeepRain. This model predicts the amount
of rainfall from weather radar data, which is three-dimensional and
four-channel data, using convolutional LSTM (ConvLSTM). ConvLSTM is a variant
of LSTM (Long Short-Term Memory) containing a convolution operation inside the
LSTM cell. For the experiment, we used radar reflectivity data for a two-year
period whose input is in a time series format in units of 6 min divided into 15
records. The output is the predicted rainfall information for the input data.
Experimental results show that two-stacked ConvLSTM reduced RMSE by 23.0%
compared to linear regression.
