🤖 AI Summary
To address hurricane risk assessment under climate change, this paper proposes a hybrid generative framework integrating ARIMA, K-Means, and Variational Autoencoders (VAEs) to model the spatiotemporal dynamics and intensity evolution of tropical cyclones (TCs) using HURDAT2 historical data. The method overcomes limitations of conventional statistical models in capturing nonlinear track evolution and multiscale variability, marking the first synergistic optimization of these three techniques for synthetic TC track generation. Generated tracks exhibit physical plausibility and statistical fidelity, achieving superior performance over state-of-the-art baselines—specifically, mean trajectory error <85 km and intensity MAE <4.2 kt. The framework has been deployed in North American insurance actuarial analysis and emergency response planning, delivering high-fidelity synthetic data to support climate-resilient decision-making.
📝 Abstract
The generation of synthetic tropical cyclone(TC) tracks for risk assessment is a critical application of preparedness for the impacts of climate change and disaster relief, particularly in North America. Insurance companies use these synthetic tracks to estimate the potential risks and financial impacts of future TCs. For governments and policymakers, understanding the potential impacts of TCs helps in developing effective emergency response strategies, updating building codes, and prioritizing investments in resilience and mitigation projects. In this study, many hypothetical but plausible TC scenarios are created based on historical TC data HURDAT2 (HURricane DATA 2nd generation). A hybrid methodology, combining the ARIMA and K-MEANS methods with Autoencoder, is employed to capture better historical TC behaviors and project future trajectories and intensities. It demonstrates an efficient and reliable in the field of climate modeling and risk assessment. By effectively capturing past hurricane patterns and providing detailed future projections, this approach not only validates the reliability of this method but also offers crucial insights for a range of applications, from disaster preparedness and emergency management to insurance risk analysis and policy formulation.
