Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan Plateau region by five learning algorithms
文献类型：期刊论文
| 作者 | Sajadi,Payam4; Sang,Yan-Fang1,4; Gholamnia,Mehdi5; Bonafoni,Stefania3; Mukherjee,Saumitra2 |
| 刊名 | Geoscience Letters
 |
| 出版日期 | 2022-02-14 |
| 卷号 | 9期号:1 |
| 关键词 | Feature selection technique Landslide susceptibility Machine learning algorithm Spatial differencing Qinghai-Tibetan Plateau |
| DOI | 10.1186/s40562-022-00218-x |
| 通讯作者 | Sang,Yan-Fang(sangyf@igsnrr.ac.cn) |
| 英文摘要 | AbstractLandslides are considered as major natural hazards that cause enormous property damages and fatalities in Qinghai-Tibetan Plateau (QTP). In this article, we evaluated the landslide susceptibility, and its spatial differencing in the whole Qinghai-Tibetan Plateau region using five state-of-the-art learning algorithms; deep neural network (DNN), logistic regression (LR), Na?ve Bayes (NB), random forest (RF), and support vector machine (SVM), differing from previous studies only in local areas of QTP. The 671 landslide events were considered, and thirteen landslide conditioning factors (LCFs) were derived for database generation, including annual rainfall, distance to drainage (Dsd)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${(\mathrm{Ds}}_{\mathrm{d}})$$\end{document}, distance to faults (Dsf)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${(\mathrm{Ds}}_{\mathrm{f}})$$\end{document}, drainage density (Dd)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{d})$$\end{document}, elevation (Elev), fault density (Fd)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({F}_{d})$$\end{document}, lithology, normalized difference vegetation index (NDVI), plan curvature (Plc)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${(\mathrm{Pl}}_{\mathrm{c}})$$\end{document}, profile curvature (Prc)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${(\mathrm{Pr}}_{\mathrm{c}})$$\end{document}, slope (S°)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${(S}^{^\circ })$$\end{document}, stream power index (SPI), and topographic wetness index (TWI). The multi-collinearity analysis and mean decrease Gini (MDG) were used to assess the suitability and predictability of these factors. Consequently, five landslide susceptibility prediction (LSP) maps were generated and validated using accuracy, area under the receiver operatic characteristic curve, sensitivity, and specificity. The MDG results demonstrated that the rainfall, elevation, and lithology were the most significant landslide conditioning factors ruling the occurrence of landslides in Qinghai-Tibetan Plateau. The LSP maps depicted that the north-northwestern and south-southeastern regions (?45% of total area). Moreover, among the five models with a high goodness-of-fit, RF model was highlighted as the superior one, by which higher accuracy of landslide susceptibility assessment and better prone areas management in QTP can be achieved compared to previous results.Graphical Abstract |
| 语种 | 英语 |
| WOS记录号 | BMC:10.1186/S40562-022-00218-X |
| 出版者 | Springer International Publishing |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/166716]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Sang,Yan-Fang |
| 作者单位 | 1.Ministry of Emergency Management of China; Key Laboratory of Compound and Chained Natural Hazards Dynamics 2.Jawaharlal Nehru University; School of Environmental Sciences 3.University of Perugia; Department of Engineering 4.Chinese Academy of Sciences; Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research 5.Sanandaj Branch; Department of Civil Engineering |
| 推荐引用方式 GB/T 7714 | Sajadi,Payam,Sang,Yan-Fang,Gholamnia,Mehdi,et al. Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan Plateau region by five learning algorithms[J]. Geoscience Letters,2022,9(1). |
| APA | Sajadi,Payam,Sang,Yan-Fang,Gholamnia,Mehdi,Bonafoni,Stefania,&Mukherjee,Saumitra.(2022).Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan Plateau region by five learning algorithms.Geoscience Letters,9(1). |
| MLA | Sajadi,Payam,et al."Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan Plateau region by five learning algorithms".Geoscience Letters 9.1(2022). |
入库方式： OAI收割
来源：地理科学与资源研究所
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