  Tropical cyclones can be of varied intensity and cause a huge loss of lives
and property if the intensity is high enough. Therefore, the prediction of the
intensity of tropical cyclones advance in time is of utmost importance. We
propose a novel stacked bidirectional long short-term memory network (BiLSTM)
based model architecture to predict the intensity of a tropical cyclone in
terms of Maximum surface sustained wind speed (MSWS). The proposed model can
predict MSWS well advance in time (up to 72 h) with very high accuracy. We have
applied the model on tropical cyclones in the North Indian Ocean from 1982 to
2018 and checked its performance on two recent tropical cyclones, namely, Fani
and Vayu. The model predicts MSWS (in knots) for the next 3, 12, 24, 36, 48,
60, and 72 hours with a mean absolute error of 1.52, 3.66, 5.88, 7.42, 8.96,
10.15, and 11.92, respectively.
