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The prediction of the intensity, location and time of the landfall of atropical cyclone well advance in time and with high accuracy can reduce humanand material loss immensely. In this article, we develop a Long Short-Termmemory based Recurrent Neural network model to predict intensity (in terms ofmaximum sustained surface wind speed), location (latitude and longitude), andtime (in hours after the observation period) of the landfall of a tropicalcyclone which originates in the North Indian ocean. The model takes as inputthe best track data of cyclone consisting of its location, pressure, seasurface temperature, and intensity for certain hours (from 12 to 36 hours)anytime during the course of the cyclone as a time series and then providepredictions with high accuracy. For example, using 24 hours data of a cycloneanytime during its course, the model provides state-of-the-art results bypredicting landfall intensity, time, latitude, and longitude with a meanabsolute error of 4.24 knots, 4.5 hours, 0.24 degree, and 0.37 degreerespectively, which resulted in a distance error of 51.7 kilometers from thelandfall location. We further check the efficacy of the model on three recentdevastating cyclones Bulbul, Fani, and Gaja, and achieved better results thanthe test dataset.

Prediction of Landfall Intensity, Location, and Time of a Tropical Cyclone