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| GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method | |
| Chen, Wei1; Xie, Xiaoshen1; Peng, Jianbing2; Shahabi, Himan3; Hong, Haoyuan4,5,6; Dieu Tien Bui7; Duan, Zhao1,8; Li, Shaojun9; Zhu, A-Xing4,5,6 | |
| 2018 | |
| 发表期刊 | CATENA
 |
| ISSN | 0341-8162 |
| 卷号 | 164期号:-页码:135-149 |
| 摘要 | Taibai County is a mountainous area in China, where rainfall-induced landslides occur frequently. The purpose of this study is to assess landslide susceptibility using the integrated Random Forest (RF) with bivariate Statistical Index (SI), the Certainty Factor (CF), and Index of Entropy (IDE). For this purpose, a total of 212 landslides for the study area were identified and collected. Of these landslides, 70% (148) were selected randomly for building the models and the other landslides (64) were used for validating the models. Accordingly, 12 landslide conditioning factors were considered that involve altitude, slope angle, plan curvature, profile curvature, slope aspect, distance to roads, distance to faults, distance to rivers, rainfall, NDVI, land use, and lithology. Then, the spatial correlation between conditioning factors and landslides was analysed using the RF method to quantify the predictive ability of these factors. In the next step, three landslide models, the RF-SI, RF-CF and RF-IOE, were constructed using the training dataset. Finally, the receiver operating characteristic (ROC) and statistical measures such as the kappa index, positive predictive rates, negative predictive rates, sensitivity, specificity, and accuracy were employed to validate and compare the predictive capability of the three models. Of the models, the RF-CF model has the highest positive predictive rate, specificity, accuracy, kappa index and AUC values of 0.838, 0.824, 0.865, 0.730 and 0.925 for the training data, and the highest positive predictive rate, negative predictive rate, sensitivity, specificity, accuracy, kappa index and AUC values of 0.896, 0.934, 0.938, 0.891, 0.914, 0.828, and 0.946 for the validation data, respectively. In general, the RF-CF model produced an optimized balance in terms of AUC values and statistical measures. |
| 关键词 | Landslide Statistical Index Certainty Factor Index of Entropy Random Forest |
| DOI | 10.1016/j.catena.2018.01.012 |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS研究方向 | Geology ; Agriculture ; Water Resources |
| WOS类目 | Geosciences, Multidisciplinary ; Soil Science ; Water Resources |
| WOS记录号 | WOS:000430031800015 |
| 出版者 | ELSEVIER SCIENCE BV |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://119.78.100.198/handle/2S6PX9GI/4264 |
| 专题 | 岩土力学所知识全产出_期刊论文 国家重点实验室知识产出_期刊论文 |
| 作者单位 | 1.Xian Univ Sci & Technol, Coll Geol & Environm; 2.Changan Univ, Dept Geol Engn; 3.Univ Kurdistan, Fac Nat Resources, Dept Geomorphol; 4.Nanjing Normal Univ, Key Lab Virtual Geog Environm; 5.State Key Lab Cultivat Base Geog Environm Evolut; 6.Jiangsu Ctr Collaborat Innovat Geog Informat Reso; 7.Univ Coll Southeast Norway, Dept Business & IT, Geog Informat Syst Grp; 8.Chengdu Univ Technol, State Key Lab Geohazard Prevent & Geoenvironm Pro ; 9.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn |
| 推荐引用方式 GB/T 7714 | Chen, Wei,Xie, Xiaoshen,Peng, Jianbing,et al. GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method[J]. CATENA,2018,164(-):135-149. |
| APA | Chen, Wei.,Xie, Xiaoshen.,Peng, Jianbing.,Shahabi, Himan.,Hong, Haoyuan.,...&Zhu, A-Xing.(2018).GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method.CATENA,164(-),135-149. |
| MLA | Chen, Wei,et al."GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method".CATENA 164.-(2018):135-149. |
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