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.
翻译:台湾的碎片流动警报系统使用时间加权的降雨量测量方法,在措施超过预定阈值时导致警报。然而,这个系统产生许多假警报,并忽略了实际碎片流动的很大一部分。为了改进这个系统,我们实施了五个机器学习模型,输入历史降雨数据,预测碎片流动是否会在选定的时间内发生。我们发现,在五个模型中,一个随机森林模型表现最佳,优于台湾的现有系统。此外,我们查明了降雨轨迹与碎片流动发生密切相关,并探索了缺失碎片流动风险与经常错误警报之间的取舍。这些结果表明,仅为拯救生命而培训小时降雨数据机器学习模型的潜力,同时减少错误警报。