  The prediction of the intensity, location and time of the landfall of a
tropical cyclone well advance in time and with high accuracy can reduce human
and material loss immensely. In this article, we develop a Long Short-Term
memory based Recurrent Neural network model to predict intensity (in terms of
maximum sustained surface wind speed), location (latitude and longitude), and
time (in hours after the observation period) of the landfall of a tropical
cyclone which originates in the North Indian ocean. The model takes as input
the best track data of cyclone consisting of its location, pressure, sea
surface temperature, and intensity for certain hours (from 12 to 36 hours)
anytime during the course of the cyclone as a time series and then provide
predictions with high accuracy. For example, using 24 hours data of a cyclone
anytime during its course, the model provides state-of-the-art results by
predicting landfall intensity, time, latitude, and longitude with a mean
absolute error of 4.24 knots, 4.5 hours, 0.24 degree, and 0.37 degree
respectively, which resulted in a distance error of 51.7 kilometers from the
landfall location. We further check the efficacy of the model on three recent
devastating cyclones Bulbul, Fani, and Gaja, and achieved better results than
the test dataset.
