Data-driven proactive prediction of pumice drifting patterns using similarity search of the Kuroshio current axis

Pumice drifting poses substantial risks to maritime navigation and coastal communities. While traditional ocean-current-based simulations effectively predict drifting patterns, they are resource-intensive and unsuitable for real-time use following abrupt eruptions. This study proposes a data-driven framework that enables rapid, low-cost pumice drift prediction, leveraging daily-reported Kuroshio Current axis (KCA) patterns and conducting similarity searches on pre-existing simulation datasets. Focusing on eruptions at Bayonnaise Rocks in the Izu Islands, Japan, we demonstrate that the dynamic time warping distance, a measure of similarity between the current KCA pattern and historical KCA patterns, accurately predicts drifting scenarios within the critical first 10 days post-eruption. This method reliably identifies high-risk cases, including those affecting coastal regions, without requiring new simulations. By refining simulation datasets and enhancing prediction accuracy, this framework can become a practical tool for hazard assessments, offering a scalable solution for proactive disaster-risk management in response to unpredictable pumice eruptions.