The Bay of Bengal is one of the most cyclone-prone regions in the world, frequently experiencing severe tropical storms that result in significant socio-economic losses. Accurate prediction of cyclone-induced damage is essential for effective disaster management and mitigation planning. This study proposes a machine learning-based approach for cyclone damage forecasting using Decision Tree algorithms. Historical meteorological data, including wind speed, atmospheric pressure, temperature, storm surge, and humidity, are analyzed to model cyclone impact. The J48 decision tree algorithm is employed to classify damage levels and evaluate regional risk. Experimental results demonstrate that the proposed model provides interpretable decision rules and satisfactory prediction accuracy. The model can assist disaster management authorities in improving early warning systems and evacuation strategies in cyclone-prone coastal regions.
N.Magesh et al. (Thu,) studied this question.