Rainfall intensity due to cyclonic events is very high compared to the monsoon rain, causing heavy damage to the lives of humans and cattle and another severe bruise. To minimize such damages, accurate prediction of rainfall is necessary. Adequate knowledge about the spatial distribution of precipitation and its temporal variation is essential for any analysis. The study aims at predicting the rainfall resulting from the tropical cyclone (TC) to help any relief activity or preparation of disaster mitigation plans. The detailed definition, classification, and conditions necessary for the cyclone to occur are discussed in the study to know how a hurricane originates, grows, and dissipates. GIS-based mean rainfall along the track of TC is derived from TRMM-3B42 data to develop a relationship between the cyclonic parameters and rainfall. This correlation helps to assess its impact and behaviour. A generalized regression model is developed with the sensitive parameters of TC-induced rainfall and cyclonic variables like wind speed, location, pressure, and precipitation to predict future events with predictors as the cyclonic parameters and rainfall as the response. Rainfall variability from 2008-2017 is analyzed. 2/3rd of the data (from 2008-17) is used in analyzing part and the remaining for validation. The prediction for the GAJA cyclone resulted in a correlation value of 0.8 and 0.72 for the 16th and 17th of November 2018. The results show that the predicted value is almost the same as the actual value of rainfall that has occurred.
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