A performance evaluation of convolutional and recurrent neural network on Philippine typhoon data

Abstract

In meteorology, neural networks have the potential to be useful for advancing forecasting and prediction capabilities, especially since some were designed to be useful for time series data like weather data. This study investigated the performance of the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Both CNN and RNN are ideal for time series classification problems. The models were designed to look back at 5 days of weather data to predict the presence or category of a typhoon (No Typhoon, Tropical Depression, Tropical Storm, Severe Tropical Storm, Typhoon, and Super Typhoon). The models were fed with weather data (obtained from NASA and PAGASA) from four locations in the Philippines with the parameters: atmospheric pressure, humidity, precipitation, temperature and wind speed. The research investigated the Accuracy, Cross Entropy Error, Precision, Recall, and F1-Measure, validated using 12-fold Rolling Basis Cross Validation. The results reveal that the CNN and RNN model performed to varying extents. The CNN model scored better at average accuracy however, the RNN model performed better at average cross entropy error, precision, recall, and F1 measure. The RNN model achieved better scores for precision on most categories while the CNN model performed better at recall and F1 measure on other categories. Both performed better at precision, recall and F1 measure on No Typhoon compared to other categories. This is likely due to the historical data being mostly composed of days with no typhoons.