| DOI: | 10.1007/s11069-021-04601-3
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| 论文题名: | Torrential rainfall-induced landslide susceptibility assessment using machine learning and statistical methods of eastern Himalaya |
| 作者: | Chowdhuri I.; Pal S.C.; Chakrabortty R.; Malik S.; Das B.; Roy P.
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| 刊名: | Natural Hazards
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| ISSN: | 0921030X
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| 出版年: | 2021
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| 卷: | 107, 期:1 | | 起始页码: | 697
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| 结束页码: | 722
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| 语种: | 英语
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| 中文关键词: | Boosted regression tree
; Ensemble methods
; Landslide susceptibility
; Logistic regression
; Random forest
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| 英文关键词: | accuracy assessment
; calibration
; landslide
; machine learning
; natural hazard
; numerical model
; rainfall
; regression analysis
; slope dynamics
; slope failure
; Himalayas
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| 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. |
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| 资源类型: | 期刊论文
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/168960
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| Appears in Collections: | 气候变化与战略
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作者单位: | Department of Geography, The University of Burdwan, Bardhaman, West Bengal 713104, India
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| Recommended Citation: | |
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]. Natural Hazards,2021-01-01,107(1)
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