  Taiwan has the highest susceptibility to and fatalities from debris flows
worldwide. The existing debris flow warning system in Taiwan, which uses a
time-weighted measure of rainfall, leads to alerts when the measure exceeds a
predefined threshold. However, this system generates many false alarms and
misses a substantial fraction of the actual debris flows. Towards improving
this system, we implemented five machine learning models that input historical
rainfall data and predict whether a debris flow will occur within a selected
time. We found that a random forest model performed the best among the five
models and outperformed the existing system in Taiwan. Furthermore, we
identified the rainfall trajectories strongly related to debris flow
occurrences and explored trade-offs between the risks of missing debris flows
versus frequent false alerts. These results suggest the potential for machine
learning models trained on hourly rainfall data alone to save lives while
reducing false alerts.
