山崩
地质学
危害
渲染(计算机图形)
随机森林
计算机科学
特征(语言学)
基本事实
遥感
数据挖掘
地震学
人工智能
语言学
化学
哲学
有机化学
作者
Kamal Rana,Uğur Öztürk,Nishant Malik
摘要
Abstract Electronic databases of landslides seldom include the triggering mechanisms, rendering these inventories unusable for landslide hazard modeling. We present a method for classifying the triggering mechanisms of landslides in existing inventories, thus, allowing these inventories to aid in landslide hazard modeling corresponding to the correct event chain. Our method uses various geometric characteristics of landslides as the feature space for the machine‐learning classifier random forest , resulting in accurate and robust classifications of landslide triggers. We applied the method to six landslide inventories spread over the Japanese archipelago in several different tests and training configurations to demonstrate the effectiveness of our approach. We achieved mean accuracy ranging from 67% to 92%. We also provide an illustrative example of a real‐world usage scenario for our method using an additional inventory with unknown ground truth. Furthermore, our feature importance analysis indicates that landslides having identical trigger mechanisms exhibit similar geometric properties.
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