| Summary: | Precise prediction of a cyclone track with wind speed, pressure, landfall point, and the time of crossing the land are essential for disaster management and mitigation, including evacuation processes. In this paper, we use an artificial neural network (ANN) approach to estimate the cyclone parameters. For this purpose, these parameters are obtained from the International Best Track Archive for Climate Stewardship (IBTrACS), from the National Oceanic and Atmospheric Administration (NOAA). Since ANN benefits from a large number of data points, each cyclone track is divided into different segments. We use past information to predict the geophysical parameters of a cyclone. The predicted values are compared with the observations.
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