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Use Ensemble Learning to Estimate the Population and Assets Exposed to Tropical Cyclones

Tropical cyclone (TC) is one of the major meteorological disasters in the world, which seriously threatens the safety of human life and property. In this study, we adopt the tropical cyclone best track archive and the global tropical cyclone exposure data sets, using the ensemble machine learning approach for exposed to the TCs of the population and asset to estimate. To the best of our knowledge, this research is the first hazards assessment study using machine learning methods to estimate the exposure of tropical cyclones to population and property end-to-end. In particular, we validated our model using data from the country most affected by tropical cyclones. The results depicted a 10% improvement over traditional methods. The correlation coefficient r between the predicted value and the exposure data is 0.813, demonstrating a strong correlation between the predicted results and the great potential of machine learning to solve the problem of hazards assessment. However, there is still uncertainty in assessing the impact of tropical cyclones, especially the impact caused by high wind speeds.