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DOI	10.3389/feart.2021.640043
	Data-Driven Landslide Nowcasting at the Global Scale
	Stanley, Thomas A.; Kirschbaum, Dalia B.; Benz, Garrett; Emberson, Robert A.; Amatya, Pukar M.; Medwedeff, William; Clark, Marin K.
通讯作者	Stanley, TA (corresponding author), Univ Space Res Assoc USRA, Columbia, MD 21046 USA. ; Stanley, TA (corresponding author), Goddard Earth Sci Technol & Res GESTAR, Greenbelt, MD 20770 USA. ; Stanley, TA (corresponding author), Goddard Space Flight Ctr, Lab Hydrol Sci, Greenbelt, MD 20771 USA.
来源期刊	FRONTIERS IN EARTH SCIENCE
EISSN	2296-6463
出版年	2021
卷号	9
英文摘要	Landslides affect nearly every country in the world each year. To better understand this global hazard, the Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed previously. LHASA version 1 combines satellite precipitation estimates with a global landslide susceptibility map to produce a gridded map of potentially hazardous areas from 60 degrees North-South every 3 h. LHASA version 1 categorizes the world's land surface into three ratings: high, moderate, and low hazard with a single decision tree that first determines if the last seven days of rainfall were intense, then evaluates landslide susceptibility. LHASA version 2 has been developed with a data-driven approach. The global susceptibility map was replaced with a collection of explanatory variables, and two new dynamically varying quantities were added: snow and soil moisture. Along with antecedent rainfall, these variables modulated the response to current daily rainfall. In addition, the Global Landslide Catalog (GLC) was supplemented with several inventories of rainfall-triggered landslide events. These factors were incorporated into the machine-learning framework XGBoost, which was trained to predict the presence or absence of landslides over the period 2015-2018, with the years 2019-2020 reserved for model evaluation. As a result of these improvements, the new global landslide nowcast was twice as likely to predict the occurrence of historical landslides as LHASA version 1, given the same global false positive rate. Furthermore, the shift to probabilistic outputs allows users to directly manage the trade-off between false negatives and false positives, which should make the nowcast useful for a greater variety of geographic settings and applications. In a retrospective analysis, the trained model ran over a global domain for 5 years, and results for LHASA version 1 and version 2 were compared. Due to the importance of rainfall and faults in LHASA version 2, nowcasts would be issued more frequently in some tropical countries, such as Colombia and Papua New Guinea; at the same time, the new version placed less emphasis on arid regions and areas far from the Pacific Rim. LHASA version 2 provides a nearly real-time view of global landslide hazard for a variety of stakeholders.
英文关键词	XGBoost machine learning IMERG SMAP GPM situational awareness antecedent rainfall tropical cyclone
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000659077600001
WOS关键词	RAINFALL THRESHOLDS ; HAZARD ; VALIDATION ; PREDICTION ; PRODUCTS ; SYSTEM ; RATES ; WELL
WOS类目	Geosciences, Multidisciplinary
WOS研究方向	Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/350277
作者单位	[Stanley, Thomas A.; Benz, Garrett; Emberson, Robert A.; Amatya, Pukar M.] Univ Space Res Assoc USRA, Columbia, MD 21046 USA; [Stanley, Thomas A.; Emberson, Robert A.; Amatya, Pukar M.] Goddard Earth Sci Technol & Res GESTAR, Greenbelt, MD 20770 USA; [Stanley, Thomas A.; Kirschbaum, Dalia B.; Benz, Garrett; Emberson, Robert A.; Amatya, Pukar M.] Goddard Space Flight Ctr, Lab Hydrol Sci, Greenbelt, MD 20771 USA; [Benz, Garrett] Univ Maryland, College Pk, MD 20742 USA; [Medwedeff, William; Clark, Marin K.] Univ Michigan, Dept Earth & Environm Sci, Ann Arbor, MI 48109 USA
推荐引用方式 GB/T 7714	Stanley, Thomas A.,Kirschbaum, Dalia B.,Benz, Garrett,et al. Data-Driven Landslide Nowcasting at the Global Scale[J],2021,9.
APA	Stanley, Thomas A..,Kirschbaum, Dalia B..,Benz, Garrett.,Emberson, Robert A..,Amatya, Pukar M..,...&Clark, Marin K..(2021).Data-Driven Landslide Nowcasting at the Global Scale.FRONTIERS IN EARTH SCIENCE,9.
MLA	Stanley, Thomas A.,et al."Data-Driven Landslide Nowcasting at the Global Scale".FRONTIERS IN EARTH SCIENCE 9(2021).