气候变化领域集成服务门户(CCPortal): Torrential rainfall-induced landslide susceptibility assessment using machine learning and statistical methods of eastern Himalaya

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DOI	10.1007/s11069-021-04601-3
	Torrential rainfall-induced landslide susceptibility assessment using machine learning and statistical methods of eastern Himalaya
	Chowdhuri I.; Pal S.C.; Chakrabortty R.; Malik S.; Das B.; Roy P.
发表日期	2021
ISSN	0921030X
起始页码	697
结束页码	722
卷号	107 期号:1
英文摘要	Landslide susceptibility predictive capabilities are believed to be varied with numerous techniques such as stand-alone statistical, stand-alone machine learning (ML), and ensemble of statistical and ML. However, the landslide susceptibility (LS) model is constantly being modified with recent progress in statistics and in ML. We used logistic regression (LR), random forest (RF), boosted regression tree (BRT), BRT-LR, and BRT-RF model for model calibration and validation. Apart from that, we used RF to measure the relative importance of landslide causative factors (LCFs). Tests were conducted to the damaged landslide patches using a number of 16 LCFs (geomorphological, hydrological, geological, and environmental). We noticed that the predicted rates are exceptional for the BRT-RF model (AUC: 0.919), whereas models of LR (0.822), RF (0.876), BRT (0.857), and BRT-LR (0.902) produced higher variations in the data set accuracy. We therefore propose that the BRT-RF model be an effective method of increasing predictive precision level of LS. This research finding can be used in other fields for planning and management by stakeholders in order to minimize the impact of landslide. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.
关键词	Boosted regression tree Ensemble methods Landslide susceptibility Logistic regression Random forest
英文关键词	accuracy assessment; calibration; landslide; machine learning; natural hazard; numerical model; rainfall; regression analysis; slope dynamics; slope failure; Himalayas
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206161
作者单位	Department of Geography, The University of Burdwan, Bardhaman, West Bengal 713104, India
推荐引用方式 GB/T 7714	Chowdhuri I.,Pal S.C.,Chakrabortty R.,et al. Torrential rainfall-induced landslide susceptibility assessment using machine learning and statistical methods of eastern Himalaya[J],2021,107(1).
APA	Chowdhuri I.,Pal S.C.,Chakrabortty R.,Malik S.,Das B.,&Roy P..(2021).Torrential rainfall-induced landslide susceptibility assessment using machine learning and statistical methods of eastern Himalaya.Natural Hazards,107(1).
MLA	Chowdhuri I.,et al."Torrential rainfall-induced landslide susceptibility assessment using machine learning and statistical methods of eastern Himalaya".Natural Hazards 107.1(2021).