A context-aware hybrid deep learning model for the prediction of tropical cyclone trajectories

Tropical cyclones (TCs) are powerful natural disasters that lead to the loss of human lives and destruction of nature. Accurate prediction of the TC trajectory over a long period is crucial for timely warning and safe evacuation. Given the complexity of TCs and the geographical contexts that affects their movements, robust tools are required to process historical TC data and predict their locations. In this study, three deep learning models with flexible topology, namely, the multilayer perceptron (MLP) model, long short-term memory (LSTM) model, and a new data-driven model (MLP-LSTM) for TC trajectory prediction, were developed. They were evaluated using the North Atlantic Ocean TC dataset and contextual information such as wind speed, wind direction, and air pressure. The results showed that the hybrid MLP-LSTM model outperformed the MLP and LSTM models, especially when contextual information was considered. The average prediction distance errors for the next three hours were calculated as 52.73 km, 20.65 km, and 19.54 km for the MLP, LSTM, and MLP-LSTM models, respectively. The prediction enhancements for the next 24 h when contextual information was considered were 166 km, 203 km, and 208 km for the MLP, LSTM, and MLP-LSTM models, respectively.

Automated summary

In this study, three deep learning models were developed for predicting the trajectory of tropical cyclones. The hybrid MLP-LSTM model performed the best, especially when contextual information was considered. It had the smallest prediction errors compared to the MLP and LSTM models.