Modeling of tropical cyclone activity over the North Indian Ocean using generalised additive model and machine learning techniques: role of Boreal summer intraseasonal oscillation

This study investigates the contribution of Boreal Summer Intraseasonal Oscillation (BSISO) to the tropical cyclone (TC) activity over the North Indian Ocean (NIO) and assesses the prediction skill of a statistical Generalised Additive Model (GAM) and two machine learning techniques—Random Forest (RF) and Support Vector Regression (SVR). Joint Typhoon Warning Centre TC and BSISO1 Index data have been used for a period of 33-year (1981–2013). By considering eight phases of BSISO, prediction models have been developed using a kernel density estimation for the TC genesis, Euler integration step to fit the tracks, and a country mask approach for the landfall across the NIO rim countries. Result shows that GAM has the highest prediction skill compared to the RF and SVR. Westward and Northward moving TCs are controlled by the wind and the TC activities during BSISO phases which modulated by wind matched well against observations over the NIO. Distance calculation validation method is applied to assess the skill of models.