Predicting cyclone landfall using mutual information and dilated recurrent neural network

Abstract Cyclones are a severe storm system with a defined center, occurring in the tropical regions. Upon landfall, it causes massive damage to both lives and the economy. With the increase in frequency and intensity of tropical cyclones occurring over the years and growing coastal settlements, the study of cyclone landfall remains of paramount importance for disaster control and mitigation. Cyclones experience rapid changes, with various environmental factors modulating the trajectory and intensity. Thus predicting cyclone landfall demands a highly precise technique coupled with knowledge of environmental parameters. With the complexity and nonlinearity of the cyclone track data, determining parameters conducive for the landfall prediction of a cyclone remains crucial for precision and knowledge of the storm system. While numerous methods have been employed for detecting causal interactions among weather systems like Granger Causality and Transfer Entropy, each comes with its limitation and computational overhead. In this work, we investigate the where and when of a cyclone landfall by studying the influencing factors regulating the location and time of a cyclone landfall over