Deep learning model for six hour rainfall prediction

This study utilized a convolutional neural network (CNN) architecture based on Convlstm and Trajgru models with CBAM method to predict rainfall. The radar and satellite data collected and preprocessed during summer seasons from 2019 to 2022 were used along with additional auxiliary data including longitude, latitude, and terrain information to perform deep learning-based rainfall prediction.
CBAM method is used to focus attention on specific spatial and channel relationships. Convlstm and Trajgru are neural network architectures designed to process spatiotemporal data.
Assuming that atmospheric flows move at a maximum speed of 72 km/hour, to predict based on information six hours ahead of time, a spatial context of 432 km in all directions, which includes a 512 km X 512 km domain, was used for training.
In order to train and validate the deep neural network, the produced radar data from the Korean Meteorological Administration was compared and evaluated. 
The model was trained to predict rainfall for the next 0-360 minutes using a combination of continuous GK2A satellite data and radar images captured every 30 minutes starting from 2 hours before the current time.
The predictive models trained with different loss functions, including Huber loss, Cosh loss, and a combination of MSE and MAE with adjustable ratios, were compared based on their performance in predicting the thresholded rainfall intensities of 0.1, 1, 4, and 9 mm/hr, as evaluated by the critical success index (CSI). 
In addition, the rainfall intensities were classified into eight categories using a segmentation technique, and the performance of the models was analyzed accordingly.