Integrating predictive modeling techniques with geospatial data for landslide susceptibility assessment in northern Pakistan
文献类型:期刊论文
作者 | Asghar, Aamir1,2,3,4; Su, Li-jun2,3,4; Zhao, Bo3,4; Usmani, Nadeem Ahmad3 |
刊名 | JOURNAL OF MOUNTAIN SCIENCE |
出版日期 | 2023-09-01 |
卷号 | 20期号:9页码:2603-2627 |
ISSN号 | 1672-6316 |
关键词 | Landslides MMMM expressway Machine learning Landslide susceptibility Northern Pakistan |
DOI | 10.1007/s11629-023-8029-2 |
英文摘要 | The eastern road section of the China-Pakistan Economic Corridor (CPEC) traverses the challenging terrain of northern Pakistan, where frequent landslides pose a significant threat to socioeconomic development and infrastructure. However, the insufficient data on landslide hazards presents a substantial challenge to practical mitigation efforts. Therefore, we conducted an extensive study to gain insight into landslide assessment along the Mansehra-Muzaffarabad-Mirpur and Mangla (MMMM) Expressway. This study involved preparing a landslide inventory, analyzing landslide causative factors, and developing landslide susceptibility models (LSMs) using published data, remote sensing interpretations, field excursions and integrated predictive techniques. We first used Pearson's correlation coefficient (PCC), variable importance factors (VIF), and information gain ratio (IGR) to evaluate multicollinearity among the selected landslide causative factors (LCFs). Then, the topographic roughness index (TRI) with VIF > 5 and PCC > 0.7 was considered a redundant factor and thus removed before the data modeling. Finally, we adopted multiple machine-learning methods to analyze landslide susceptibility. The results indicate that the landslide inventory contains 1,776 events, of which 674 were classified based on geometrical and lithological configurations. The IGR results show that the rainfall, lithology, PGA, drainage density, slope, and distance to fault are the most effective LCFs. The AUC values for random forest (RF) (0.901), extreme gradient boosting (XGBoost) (0.884), and K-nearest neighbor (KNN) (0.872) remained higher than evidential belief function (EBF) (0.833), weight of evidence (WoE) (0.820), and certainty factor (CF) (0.810), respectively. The RF model outperformed all other models in terms of prediction. However, these models are accurate but newer in the area; thus, susceptible zones were verified with comprehensive field investigations. The northern and central regions accounted for the high and very high susceptibility classes in the final landslide susceptibility mapping (LSM) compared to the southern areas. |
WOS关键词 | HAZARA-KASHMIR SYNTAXIS ; BALA ROCK AVALANCHE ; SPATIAL PREDICTION ; EARTHQUAKE ; HIMALAYAS ; CLASSIFICATION |
资助项目 | This study has been supported by the National Natural Science Foundation of China (Grant No. U22A20603), and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA20030301). The authors express their gratitude for this fi[U22A20603] ; National Natural Science Foundation of China[XDA20030301] ; Strategic Priority Research Program of the Chinese Academy of Sciences |
WOS研究方向 | Environmental Sciences & Ecology |
语种 | 英语 |
出版者 | SCIENCE PRESS |
WOS记录号 | WOS:001080350100010 |
资助机构 | This study has been supported by the National Natural Science Foundation of China (Grant No. U22A20603), and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA20030301). The authors express their gratitude for this fi ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences |
源URL | [http://ir.imde.ac.cn/handle/131551/57681] |
专题 | 成都山地灾害与环境研究所_山地灾害与地表过程重点实验室 |
通讯作者 | Su, Li-jun |
作者单位 | 1.Univ Poonch Rawalakot, Rawalakot 12350, Pakistan 2.CAS HEC, China Pakistan Joint Res Ctr Earth Sci, Islamabad 45320, Pakistan 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Mt Hazards & Environm, Key Lab Mt Hazards & Earth Surface Proc, Chengdu 610041, Peoples R China |
推荐引用方式 GB/T 7714 | Asghar, Aamir,Su, Li-jun,Zhao, Bo,et al. Integrating predictive modeling techniques with geospatial data for landslide susceptibility assessment in northern Pakistan[J]. JOURNAL OF MOUNTAIN SCIENCE,2023,20(9):2603-2627. |
APA | Asghar, Aamir,Su, Li-jun,Zhao, Bo,&Usmani, Nadeem Ahmad.(2023).Integrating predictive modeling techniques with geospatial data for landslide susceptibility assessment in northern Pakistan.JOURNAL OF MOUNTAIN SCIENCE,20(9),2603-2627. |
MLA | Asghar, Aamir,et al."Integrating predictive modeling techniques with geospatial data for landslide susceptibility assessment in northern Pakistan".JOURNAL OF MOUNTAIN SCIENCE 20.9(2023):2603-2627. |
入库方式: OAI收割
来源:成都山地灾害与环境研究所
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