Assessing Debris-Flow Susceptibility at Local and Global Scales: A Deep-Learning-Based Comparative Study ofSichuan, China, and Worldwide

Debris flows pose a significant global geohazard, causing a large number of deaths and infrastructure damage every year. Effective protection and land-use planning in the affected regions requires understanding susceptibility to these events. Although a global phenomenon, previous studies have focused extensively on local areas with specialized models and accordingly complex feature selections. In this study, we investigate whether a unified debris-flow susceptibility prediction paradigm can be achieved regardless of regional scale, using only very few global public remote sensing data sources. To this end, this work contributes in the following ways: (1) A novel two-step negative sample generation scheme is proposed, and two open debris-flow datasets are constructed based on global debris-flow locations and locations in Sichuan, China. (2) An open-source end-to-end machine learning platform using remote sensing features directly is proposed, which achieves state-of-the-art results with 0.947 and 0.957 AUC in both scales compared to 0.88 for previous methods on the same location data, while using far fewer features. (3) A comparative feature importance analysis shows that, given the significant feature distribution difference on global vs local datasets, alleviating the scale-level gap is possible by leveraging the advanced deep learning technologies. This allows our unified framework to be easily applied to any regional study of debris-flow susceptibility prediction.