Improving debris flow evacuation alerts in Taiwan using machine learning
This work addresses a critical public safety issue for communities in Taiwan prone to debris flows, representing an incremental improvement over existing methods.
The authors tackled the problem of high false alarm rates and missed debris flows in Taiwan's existing rainfall-based warning system by implementing five machine learning models, finding that a random forest model outperformed the current system.
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.