山崩
自然灾害
比例(比率)
启发式
地理信息系统
地图学
水文地质学
环境资源管理
计算机科学
数据挖掘
地质学
地理
气象学
环境科学
人工智能
地貌学
岩土工程
作者
Thomas Stanley,Dalia Kirschbaum
出处
期刊:Natural Hazards
[Springer Nature]
日期:2017-02-07
卷期号:87 (1): 145-164
被引量:173
标识
DOI:10.1007/s11069-017-2757-y
摘要
Landslides can have significant and pervasive impacts to life and property around the world. Several attempts have been made to predict the geographic distribution of landslide activity at continental and global scales. These efforts shared common traits such as resolution, modeling approach, and explanatory variables. The lessons learned from prior research have been applied to build a new global susceptibility map from existing and previously unavailable data. Data on slope, faults, geology, forest loss, and road networks were combined using a heuristic fuzzy approach. The map was evaluated with a Global Landslide Catalog developed at the National Aeronautics and Space Administration, as well as several local landslide inventories. Comparisons to similar susceptibility maps suggest that the subjective methods commonly used at this scale are, for the most part, reproducible. However, comparisons of landslide susceptibility across spatial scales must take into account the susceptibility of the local subset relative to the larger study area. The new global landslide susceptibility map is intended for use in disaster planning, situational awareness, and for incorporation into global decision support systems.
科研通智能强力驱动
Strongly Powered by AbleSci AI