The Role of Artificial Neural Networks (ANNs) in the Simulation of Tropical Cyclones in the South Indian Ocean.

This study examines projected changes in the characteristics of tropical cyclones (TCs) in the South Indian Ocean (SIO) under a future warming scenario. Using methodologies like "pseudo global warming" and the application of Artificial Neural Networks (ANNs), the analysis reveals a scenario of progressively elevated risk. Projections indicate a mean increase of 6.5% in TC intensity, accompanied by a 33.8% increase in median rain rate, which intensifies the risk of floods and landslides. Additionally, a 2° poleward shift in the Latitude of Maximum Intensity (LMI) is anticipated, exposing new coastal areas to extreme events. Although models suggest a slight 9.2% reduction in the radius of 17.5 m/s winds, the concentration of damage within this smaller area can exacerbate its severity. These phenomena, combined with rising sea levels and higher wave heights, considerably heighten the vulnerability of coastal zones. The application of ANNs proves promising for capturing the non-linear complexity of these systems, but challenges persist in the accuracy of long-term forecasts. The integration of robust climatological analyses with the continuous validation of predictive models is therefore essential for strategic adaptation planning and risk mitigation in vulnerable SIO nations.